{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='images/aiayn.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Transformer from [\"Attention is All You Need\"](https://arxiv.org/abs/1706.03762) has been on a lot of people's minds over the last year. Besides producing major improvements in translation quality, it provides a new architecture for many other NLP tasks. The paper itself is very clearly written, but the conventional wisdom has been that it is quite difficult to implement correctly. \n",
    "\n",
    "In this post I present an \"annotated\" version of the paper in the form of a line-by-line implementation. I have reordered and deleted some sections from the original paper and added comments throughout. This document itself is a working notebook, and should be a completely usable implementation. In total there are 400 lines of library code which can process 27,000 tokens per second on 4 GPUs. \n",
    "\n",
    "To follow along you will first need to install [PyTorch](http://pytorch.org/). The complete notebook is also available on [github](https://github.com/harvardnlp/annotated-transformer) or on Google [Colab](https://drive.google.com/file/d/1xQXSv6mtAOLXxEMi8RvaW8TW-7bvYBDF/view?usp=sharing). \n",
    "\n",
    "Note this is merely a starting point for researchers and interested developers. The code here is based heavily on our [OpenNMT](http://opennmt.net) packages. (If helpful feel free to [cite](#conclusion).) For other full-sevice implementations of the model check-out [Tensor2Tensor](https://github.com/tensorflow/tensor2tensor) (tensorflow) and [Sockeye](https://github.com/awslabs/sockeye) (mxnet).\n",
    "\n",
    "- Alexander Rush ([@harvardnlp](https://twitter.com/harvardnlp) or srush@seas.harvard.edu)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prelims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# !pip install http://download.pytorch.org/whl/cu80/torch-0.3.0.post4-cp36-cp36m-linux_x86_64.whl numpy matplotlib spacy torchtext seaborn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import math, copy, time\n",
    "from torch.autograd import Variable\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "seaborn.set_context(context=\"talk\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table of Contents\n",
    "\n",
    "\n",
    "* Table of Contents                               \n",
    "{:toc}      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> My comments are blockquoted. The main text is all from the paper itself."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU, ByteNet and ConvS2S, all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention.\n",
    "\n",
    "Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations. End-to-end memory networks are based on a recurrent attention mechanism instead of sequencealigned recurrence and have been shown to perform well on simple-language question answering and\n",
    "language modeling tasks.\n",
    "\n",
    "To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence aligned RNNs or convolution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most competitive neural sequence transduction models have an encoder-decoder structure [(cite)](https://arxiv.org/abs/1409.0473). Here, the encoder maps an input sequence of symbol representations $(x_1, ..., x_n)$ to a sequence of continuous representations $\\mathbf{z} = (z_1, ..., z_n)$. Given $\\mathbf{z}$, the decoder then generates an output sequence $(y_1,...,y_m)$ of symbols one element at a time. At each step the model is auto-regressive [(cite)](https://arxiv.org/abs/1308.0850), consuming the previously generated symbols as additional input when generating the next. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class EncoderDecoder(nn.Module):\n",
    "    \"\"\"\n",
    "    A standard Encoder-Decoder architecture. Base for this and many \n",
    "    other models.\n",
    "    \"\"\"\n",
    "    def __init__(self, encoder, decoder, src_embed, tgt_embed, generator):\n",
    "        super(EncoderDecoder, self).__init__()\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.src_embed = src_embed\n",
    "        self.tgt_embed = tgt_embed\n",
    "        self.generator = generator\n",
    "        \n",
    "    def forward(self, src, tgt, src_mask, tgt_mask):\n",
    "        \"Take in and process masked src and target sequences.\"\n",
    "        return self.decode(self.encode(src, src_mask), src_mask,\n",
    "                            tgt, tgt_mask)\n",
    "    \n",
    "    def encode(self, src, src_mask):\n",
    "        return self.encoder(self.src_embed(src), src_mask)\n",
    "    \n",
    "    def decode(self, memory, src_mask, tgt, tgt_mask):\n",
    "        return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    \"Define standard linear + softmax generation step.\"\n",
    "    def __init__(self, d_model, vocab):\n",
    "        super(Generator, self).__init__()\n",
    "        self.proj = nn.Linear(d_model, vocab)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return F.log_softmax(self.proj(x), dim=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1, respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='images/ModalNet-21.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoder and Decoder Stacks   \n",
    "\n",
    "### Encoder\n",
    "\n",
    "The encoder is composed of a stack of $N=6$ identical layers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clones(module, N):\n",
    "    \"Produce N identical layers.\"\n",
    "    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    \"Core encoder is a stack of N layers\"\n",
    "    def __init__(self, layer, N):\n",
    "        super(Encoder, self).__init__()\n",
    "        self.layers = clones(layer, N)\n",
    "        self.norm = LayerNorm(layer.size)\n",
    "        \n",
    "    def forward(self, x, mask):\n",
    "        \"Pass the input (and mask) through each layer in turn.\"\n",
    "        for layer in self.layers:\n",
    "            x = layer(x, mask)\n",
    "        return self.norm(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We employ a residual connection [(cite)](https://arxiv.org/abs/1512.03385) around each of the two sub-layers, followed by layer normalization [(cite)](https://arxiv.org/abs/1607.06450).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class LayerNorm(nn.Module):\n",
    "    \"Construct a layernorm module (See citation for details).\"\n",
    "    def __init__(self, features, eps=1e-6):\n",
    "        super(LayerNorm, self).__init__()\n",
    "        self.a_2 = nn.Parameter(torch.ones(features))\n",
    "        self.b_2 = nn.Parameter(torch.zeros(features))\n",
    "        self.eps = eps\n",
    "\n",
    "    def forward(self, x):\n",
    "        mean = x.mean(-1, keepdim=True)\n",
    "        std = x.std(-1, keepdim=True)\n",
    "        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is, the output of each sub-layer is $\\mathrm{LayerNorm}(x + \\mathrm{Sublayer}(x))$, where $\\mathrm{Sublayer}(x)$ is the function implemented by the sub-layer itself.  We apply dropout [(cite)](http://jmlr.org/papers/v15/srivastava14a.html) to the output of each sub-layer, before it is added to the sub-layer input and normalized.  \n",
    "\n",
    "To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension $d_{\\text{model}}=512$.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SublayerConnection(nn.Module):\n",
    "    \"\"\"\n",
    "    A residual connection followed by a layer norm.\n",
    "    Note for code simplicity the norm is first as opposed to last.\n",
    "    \"\"\"\n",
    "    def __init__(self, size, dropout):\n",
    "        super(SublayerConnection, self).__init__()\n",
    "        self.norm = LayerNorm(size)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "    def forward(self, x, sublayer):\n",
    "        \"Apply residual connection to any sublayer with the same size.\"\n",
    "        return x + self.dropout(sublayer(self.norm(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-wise fully connected feed-forward network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class EncoderLayer(nn.Module):\n",
    "    \"Encoder is made up of self-attn and feed forward (defined below)\"\n",
    "    def __init__(self, size, self_attn, feed_forward, dropout):\n",
    "        super(EncoderLayer, self).__init__()\n",
    "        self.self_attn = self_attn\n",
    "        self.feed_forward = feed_forward\n",
    "        self.sublayer = clones(SublayerConnection(size, dropout), 2)\n",
    "        self.size = size\n",
    "\n",
    "    def forward(self, x, mask):\n",
    "        \"Follow Figure 1 (left) for connections.\"\n",
    "        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))\n",
    "        return self.sublayer[1](x, self.feed_forward)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decoder\n",
    "\n",
    "The decoder is also composed of a stack of $N=6$ identical layers.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Decoder(nn.Module):\n",
    "    \"Generic N layer decoder with masking.\"\n",
    "    def __init__(self, layer, N):\n",
    "        super(Decoder, self).__init__()\n",
    "        self.layers = clones(layer, N)\n",
    "        self.norm = LayerNorm(layer.size)\n",
    "        \n",
    "    def forward(self, x, memory, src_mask, tgt_mask):\n",
    "        for layer in self.layers:\n",
    "            x = layer(x, memory, src_mask, tgt_mask)\n",
    "        return self.norm(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack.  Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class DecoderLayer(nn.Module):\n",
    "    \"Decoder is made of self-attn, src-attn, and feed forward (defined below)\"\n",
    "    def __init__(self, size, self_attn, src_attn, feed_forward, dropout):\n",
    "        super(DecoderLayer, self).__init__()\n",
    "        self.size = size\n",
    "        self.self_attn = self_attn\n",
    "        self.src_attn = src_attn\n",
    "        self.feed_forward = feed_forward\n",
    "        self.sublayer = clones(SublayerConnection(size, dropout), 3)\n",
    " \n",
    "    def forward(self, x, memory, src_mask, tgt_mask):\n",
    "        \"Follow Figure 1 (right) for connections.\"\n",
    "        m = memory\n",
    "        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask))\n",
    "        x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask))\n",
    "        return self.sublayer[2](x, self.feed_forward)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions.  This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position $i$ can depend only on the known outputs at positions less than $i$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def subsequent_mask(size):\n",
    "    \"Mask out subsequent positions.\"\n",
    "    attn_shape = (1, size, size)\n",
    "    subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')\n",
    "    return torch.from_numpy(subsequent_mask) == 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Below the attention mask shows the position each tgt word (row) is allowed to look at (column). Words are blocked for attending to future words during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b0600844ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.imshow(subsequent_mask(20)[0])\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attention                                                                                                                                                                                                                                                                             \n",
    "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors.  The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.                                                                                                                                                                                                                                                                                   \n",
    "\n",
    "We call our particular attention \"Scaled Dot-Product Attention\".   The input consists of queries and keys of dimension $d_k$, and values of dimension $d_v$.  We compute the dot products of the query with all keys, divide each by $\\sqrt{d_k}$, and apply a softmax function to obtain the weights on the values.                                                                                                                                                                                                                                  \n",
    "                                                                                                                                                                     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='images/ModalNet-19.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix $Q$.   The keys and values are also packed together into matrices $K$ and $V$.  We compute the matrix of outputs as:                      \n",
    "                                                                 \n",
    "$$                                                                         \n",
    "   \\mathrm{Attention}(Q, K, V) = \\mathrm{softmax}(\\frac{QK^T}{\\sqrt{d_k}})V               \n",
    "$$   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def attention(query, key, value, mask=None, dropout=None):\n",
    "    \"Compute 'Scaled Dot Product Attention'\"\n",
    "    d_k = query.size(-1)\n",
    "    scores = torch.matmul(query, key.transpose(-2, -1)) \\\n",
    "             / math.sqrt(d_k)\n",
    "    if mask is not None:\n",
    "        scores = scores.masked_fill(mask == 0, -1e9)\n",
    "    p_attn = F.softmax(scores, dim = -1)\n",
    "    if dropout is not None:\n",
    "        p_attn = dropout(p_attn)\n",
    "    return torch.matmul(p_attn, value), p_attn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two most commonly used attention functions are additive attention [(cite)](https://arxiv.org/abs/1409.0473), and dot-product (multiplicative) attention.  Dot-product attention is identical to our algorithm, except for the scaling factor of $\\frac{1}{\\sqrt{d_k}}$. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer.  While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.                                                                                             \n",
    "\n",
    "                                                                        \n",
    "While for small values of $d_k$ the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of $d_k$ [(cite)](https://arxiv.org/abs/1703.03906). We suspect that for large values of $d_k$, the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients  (To illustrate why the dot products get large, assume that the components of $q$ and $k$ are independent random variables with mean $0$ and variance $1$.  Then their dot product, $q \\cdot k = \\sum_{i=1}^{d_k} q_ik_i$, has mean $0$ and variance $d_k$.). To counteract this effect, we scale the dot products by $\\frac{1}{\\sqrt{d_k}}$.          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='images/ModalNet-20.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.                                            \n",
    "$$    \n",
    "\\mathrm{MultiHead}(Q, K, V) = \\mathrm{Concat}(\\mathrm{head_1}, ..., \\mathrm{head_h})W^O    \\\\                                           \n",
    "    \\text{where}~\\mathrm{head_i} = \\mathrm{Attention}(QW^Q_i, KW^K_i, VW^V_i)                                \n",
    "$$                                                                                                                 \n",
    "\n",
    "Where the projections are parameter matrices $W^Q_i \\in \\mathbb{R}^{d_{\\text{model}} \\times d_k}$, $W^K_i \\in \\mathbb{R}^{d_{\\text{model}} \\times d_k}$, $W^V_i \\in \\mathbb{R}^{d_{\\text{model}} \\times d_v}$ and $W^O \\in \\mathbb{R}^{hd_v \\times d_{\\text{model}}}$.                                                                                                                                                                                             In this work we employ $h=8$ parallel attention layers, or heads. For each of these we use $d_k=d_v=d_{\\text{model}}/h=64$. Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MultiHeadedAttention(nn.Module):\n",
    "    def __init__(self, h, d_model, dropout=0.1):\n",
    "        \"Take in model size and number of heads.\"\n",
    "        super(MultiHeadedAttention, self).__init__()\n",
    "        assert d_model % h == 0\n",
    "        # We assume d_v always equals d_k\n",
    "        self.d_k = d_model // h\n",
    "        self.h = h\n",
    "        self.linears = clones(nn.Linear(d_model, d_model), 4)\n",
    "        self.attn = None\n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "        \n",
    "    def forward(self, query, key, value, mask=None):\n",
    "        \"Implements Figure 2\"\n",
    "        if mask is not None:\n",
    "            # Same mask applied to all h heads.\n",
    "            mask = mask.unsqueeze(1)\n",
    "        nbatches = query.size(0)\n",
    "        \n",
    "        # 1) Do all the linear projections in batch from d_model => h x d_k \n",
    "        query, key, value = \\\n",
    "            [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)\n",
    "             for l, x in zip(self.linears, (query, key, value))]\n",
    "        \n",
    "        # 2) Apply attention on all the projected vectors in batch. \n",
    "        x, self.attn = attention(query, key, value, mask=mask, \n",
    "                                 dropout=self.dropout)\n",
    "        \n",
    "        # 3) \"Concat\" using a view and apply a final linear. \n",
    "        x = x.transpose(1, 2).contiguous() \\\n",
    "             .view(nbatches, -1, self.h * self.d_k)\n",
    "        return self.linears[-1](x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applications of Attention in our Model                                                                                                                                                      \n",
    "The Transformer uses multi-head attention in three different ways:                                                        \n",
    "1) In \"encoder-decoder attention\" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder.   This allows every position in the decoder to attend over all positions in the input sequence.  This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as [(cite)](https://arxiv.org/abs/1609.08144).    \n",
    "\n",
    "\n",
    "2) The encoder contains self-attention layers.  In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder.   Each position in the encoder can attend to all positions in the previous layer of the encoder.                                                   \n",
    "\n",
    "\n",
    "3) Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position.  We need to prevent leftward information flow in the decoder to preserve the auto-regressive property.  We implement this inside of scaled dot-product attention by masking out (setting to $-\\infty$) all values in the input of the softmax which correspond to illegal connections.                                                                                                                                                                                                                                                      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Position-wise Feed-Forward Networks                                                                                                                                                                                                                                                                                                                                                             \n",
    "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          \n",
    "In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically.  This consists of two linear transformations with a ReLU activation in between.\n",
    "\n",
    "$$\\mathrm{FFN}(x)=\\max(0, xW_1 + b_1) W_2 + b_2$$                                                                                                                                                                                                                                                         \n",
    "                                                                                                                                                                                                                                                        \n",
    "While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1.  The dimensionality of input and output is $d_{\\text{model}}=512$, and the inner-layer has dimensionality $d_{ff}=2048$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class PositionwiseFeedForward(nn.Module):\n",
    "    \"Implements FFN equation.\"\n",
    "    def __init__(self, d_model, d_ff, dropout=0.1):\n",
    "        super(PositionwiseFeedForward, self).__init__()\n",
    "        self.w_1 = nn.Linear(d_model, d_ff)\n",
    "        self.w_2 = nn.Linear(d_ff, d_model)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.w_2(self.dropout(F.relu(self.w_1(x))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embeddings and Softmax                                                                                                                                                                                                                                                                                           \n",
    "Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension $d_{\\text{model}}$.  We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities.  In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to [(cite)](https://arxiv.org/abs/1608.05859). In the embedding layers, we multiply those weights by $\\sqrt{d_{\\text{model}}}$.                                                                                                                                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Embeddings(nn.Module):\n",
    "    def __init__(self, d_model, vocab):\n",
    "        super(Embeddings, self).__init__()\n",
    "        self.lut = nn.Embedding(vocab, d_model)\n",
    "        self.d_model = d_model\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.lut(x) * math.sqrt(self.d_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Positional Encoding                                                                                                                             \n",
    "Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence.  To this end, we add \"positional encodings\" to the input embeddings at the bottoms of the encoder and decoder stacks.  The positional encodings have the same dimension $d_{\\text{model}}$ as the embeddings, so that the two can be summed.   There are many choices of positional encodings, learned and fixed [(cite)](https://arxiv.org/pdf/1705.03122.pdf). \n",
    "\n",
    "In this work, we use sine and cosine functions of different frequencies:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n",
    "$$PE_{(pos,2i)} = sin(pos / 10000^{2i/d_{\\text{model}}})$$\n",
    "\n",
    "$$PE_{(pos,2i+1)} = cos(pos / 10000^{2i/d_{\\text{model}}})$$                                                                                                                                                                                                                                                        \n",
    "where $pos$ is the position and $i$ is the dimension.  That is, each dimension of the positional encoding corresponds to a sinusoid.  The wavelengths form a geometric progression from $2\\pi$ to $10000 \\cdot 2\\pi$.  We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $PE_{pos+k}$ can be represented as a linear function of $PE_{pos}$. \n",
    "\n",
    "In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks.  For the base model, we use a rate of $P_{drop}=0.1$. \n",
    "                                                                                                                                                                                                                                                    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class PositionalEncoding(nn.Module):\n",
    "    \"Implement the PE function.\"\n",
    "    def __init__(self, d_model, dropout, max_len=5000):\n",
    "        super(PositionalEncoding, self).__init__()\n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "        \n",
    "        # Compute the positional encodings once in log space.\n",
    "        pe = torch.zeros(max_len, d_model)\n",
    "        position = torch.arange(0, max_len).unsqueeze(1)\n",
    "        div_term = torch.exp(torch.arange(0, d_model, 2) *\n",
    "                             -(math.log(10000.0) / d_model))\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)\n",
    "        pe = pe.unsqueeze(0)\n",
    "        self.register_buffer('pe', pe)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = x + Variable(self.pe[:, :x.size(1)], \n",
    "                         requires_grad=False)\n",
    "        return self.dropout(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Below the positional encoding will add in a sine wave based on position. The frequency and offset of the wave is different for each dimension. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "pe = PositionalEncoding(20, 0)\n",
    "y = pe.forward(Variable(torch.zeros(1, 100, 20)))\n",
    "plt.plot(np.arange(100), y[0, :, 4:8].data.numpy())\n",
    "plt.legend([\"dim %d\"%p for p in [4,5,6,7]])\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also experimented with using learned positional embeddings [(cite)](https://arxiv.org/pdf/1705.03122.pdf) instead, and found that the two versions produced nearly identical results.  We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full Model\n",
    "\n",
    "> Here we define a function from hyperparameters to a full model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_model(src_vocab, tgt_vocab, N=6, \n",
    "               d_model=512, d_ff=2048, h=8, dropout=0.1):\n",
    "    \"Helper: Construct a model from hyperparameters.\"\n",
    "    c = copy.deepcopy\n",
    "    attn = MultiHeadedAttention(h, d_model)\n",
    "    ff = PositionwiseFeedForward(d_model, d_ff, dropout)\n",
    "    position = PositionalEncoding(d_model, dropout)\n",
    "    model = EncoderDecoder(\n",
    "        Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),\n",
    "        Decoder(DecoderLayer(d_model, c(attn), c(attn), \n",
    "                             c(ff), dropout), N),\n",
    "        nn.Sequential(Embeddings(d_model, src_vocab), c(position)),\n",
    "        nn.Sequential(Embeddings(d_model, tgt_vocab), c(position)),\n",
    "        Generator(d_model, tgt_vocab))\n",
    "    \n",
    "    # This was important from their code. \n",
    "    # Initialize parameters with Glorot / fan_avg.\n",
    "    for p in model.parameters():\n",
    "        if p.dim() > 1:\n",
    "            nn.init.xavier_uniform(p)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Small example model.\n",
    "tmp_model = make_model(10, 10, 2)\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training\n",
    "\n",
    "This section describes the training regime for our models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We stop for a quick interlude to introduce some of the tools \n",
    "needed to train a standard encoder decoder model. First we define a batch object that holds the src and target sentences for training, as well as constructing the masks. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batches and Masking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Batch:\n",
    "    \"Object for holding a batch of data with mask during training.\"\n",
    "    def __init__(self, src, trg=None, pad=0):\n",
    "        self.src = src\n",
    "        self.src_mask = (src != pad).unsqueeze(-2)\n",
    "        if trg is not None:\n",
    "            self.trg = trg[:, :-1]\n",
    "            self.trg_y = trg[:, 1:]\n",
    "            self.trg_mask = \\\n",
    "                self.make_std_mask(self.trg, pad)\n",
    "            self.ntokens = (self.trg_y != pad).data.sum()\n",
    "    \n",
    "    @staticmethod\n",
    "    def make_std_mask(tgt, pad):\n",
    "        \"Create a mask to hide padding and future words.\"\n",
    "        tgt_mask = (tgt != pad).unsqueeze(-2)\n",
    "        tgt_mask = tgt_mask & Variable(\n",
    "            subsequent_mask(tgt.size(-1)).type_as(tgt_mask.data))\n",
    "        return tgt_mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Next we create a generic training and scoring function to keep track of loss. We pass in a generic loss compute function that also handles parameter updates. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def run_epoch(data_iter, model, loss_compute):\n",
    "    \"Standard Training and Logging Function\"\n",
    "    start = time.time()\n",
    "    total_tokens = 0\n",
    "    total_loss = 0\n",
    "    tokens = 0\n",
    "    for i, batch in enumerate(data_iter):\n",
    "        out = model.forward(batch.src, batch.trg, \n",
    "                            batch.src_mask, batch.trg_mask)\n",
    "        loss = loss_compute(out, batch.trg_y, batch.ntokens)\n",
    "        total_loss += loss\n",
    "        total_tokens += batch.ntokens\n",
    "        tokens += batch.ntokens\n",
    "        if i % 50 == 1:\n",
    "            elapsed = time.time() - start\n",
    "            print(\"Epoch Step: %d Loss: %f Tokens per Sec: %f\" %\n",
    "                    (i, loss / batch.ntokens, tokens / elapsed))\n",
    "            start = time.time()\n",
    "            tokens = 0\n",
    "    return total_loss / total_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Data and Batching\n",
    "\n",
    "We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs.  Sentences were encoded using byte-pair encoding, which has a shared source-target vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary.\n",
    "\n",
    "\n",
    "Sentence pairs were batched together by approximate sequence length.  Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We will use torch text for batching. This is discussed in more detail below. Here we create batches in a torchtext function that ensures our batch size padded to the maximum batchsize does not surpass a threshold (25000 if we have 8 gpus)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "global max_src_in_batch, max_tgt_in_batch\n",
    "def batch_size_fn(new, count, sofar):\n",
    "    \"Keep augmenting batch and calculate total number of tokens + padding.\"\n",
    "    global max_src_in_batch, max_tgt_in_batch\n",
    "    if count == 1:\n",
    "        max_src_in_batch = 0\n",
    "        max_tgt_in_batch = 0\n",
    "    max_src_in_batch = max(max_src_in_batch,  len(new.src))\n",
    "    max_tgt_in_batch = max(max_tgt_in_batch,  len(new.trg) + 2)\n",
    "    src_elements = count * max_src_in_batch\n",
    "    tgt_elements = count * max_tgt_in_batch\n",
    "    return max(src_elements, tgt_elements)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hardware and Schedule                                                                                                                                                                                                   \n",
    "We trained our models on one machine with 8 NVIDIA P100 GPUs.  For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds.  We trained the base models for a total of 100,000 steps or 12 hours. For our big models, step time was 1.0 seconds.  The big models were trained for 300,000 steps (3.5 days)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizer\n",
    "\n",
    "We used the Adam optimizer [(cite)](https://arxiv.org/abs/1412.6980) with $\\beta_1=0.9$, $\\beta_2=0.98$ and $\\epsilon=10^{-9}$.  We varied the learning rate over the course of training, according to the formula:                                                                                            \n",
    "$$                                                                                                                                                                                                                                                                                         \n",
    "lrate = d_{\\text{model}}^{-0.5} \\cdot                                                                                                                                                                                                                                                                                                \n",
    "  \\min({step\\_num}^{-0.5},                                                                                                                                                                                                                                                                                                  \n",
    "    {step\\_num} \\cdot {warmup\\_steps}^{-1.5})                                                                                                                                                                                                                                                                               \n",
    "$$                                                                                                                                                                                             \n",
    "This corresponds to increasing the learning rate linearly for the first $warmup\\_steps$ training steps, and decreasing it thereafter proportionally to the inverse square root of the step number.  We used $warmup\\_steps=4000$.                            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: This part is very important. Need to train with this setup of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "class NoamOpt:\n",
    "    \"Optim wrapper that implements rate.\"\n",
    "    def __init__(self, model_size, factor, warmup, optimizer):\n",
    "        self.optimizer = optimizer\n",
    "        self._step = 0\n",
    "        self.warmup = warmup\n",
    "        self.factor = factor\n",
    "        self.model_size = model_size\n",
    "        self._rate = 0\n",
    "        \n",
    "    def step(self):\n",
    "        \"Update parameters and rate\"\n",
    "        self._step += 1\n",
    "        rate = self.rate()\n",
    "        for p in self.optimizer.param_groups:\n",
    "            p['lr'] = rate\n",
    "        self._rate = rate\n",
    "        self.optimizer.step()\n",
    "        \n",
    "    def rate(self, step = None):\n",
    "        \"Implement `lrate` above\"\n",
    "        if step is None:\n",
    "            step = self._step\n",
    "        return self.factor * \\\n",
    "            (self.model_size ** (-0.5) *\n",
    "            min(step ** (-0.5), step * self.warmup ** (-1.5)))\n",
    "        \n",
    "def get_std_opt(model):\n",
    "    return NoamOpt(model.src_embed[0].d_model, 2, 4000,\n",
    "            torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "> Example of the curves of this model for different model sizes and for optimization hyperparameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b060c9f0898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Three settings of the lrate hyperparameters.\n",
    "opts = [NoamOpt(512, 1, 4000, None), \n",
    "        NoamOpt(512, 1, 8000, None),\n",
    "        NoamOpt(256, 1, 4000, None)]\n",
    "plt.plot(np.arange(1, 20000), [[opt.rate(i) for opt in opts] for i in range(1, 20000)])\n",
    "plt.legend([\"512:4000\", \"512:8000\", \"256:4000\"])\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regularization                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          \n",
    "                                                                                                                                                                                                                                                                                                                      \n",
    "### Label Smoothing\n",
    "\n",
    "During training, we employed label smoothing of value $\\epsilon_{ls}=0.1$ [(cite)](https://arxiv.org/abs/1512.00567).  This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We implement label smoothing using the KL div loss. Instead of using a one-hot target distribution, we create a distribution that has `confidence` of the correct word and the rest of the `smoothing` mass distributed throughout the vocabulary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class LabelSmoothing(nn.Module):\n",
    "    \"Implement label smoothing.\"\n",
    "    def __init__(self, size, padding_idx, smoothing=0.0):\n",
    "        super(LabelSmoothing, self).__init__()\n",
    "        self.criterion = nn.KLDivLoss(size_average=False)\n",
    "        self.padding_idx = padding_idx\n",
    "        self.confidence = 1.0 - smoothing\n",
    "        self.smoothing = smoothing\n",
    "        self.size = size\n",
    "        self.true_dist = None\n",
    "        \n",
    "    def forward(self, x, target):\n",
    "        assert x.size(1) == self.size\n",
    "        true_dist = x.data.clone()\n",
    "        true_dist.fill_(self.smoothing / (self.size - 2))\n",
    "        true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)\n",
    "        true_dist[:, self.padding_idx] = 0\n",
    "        mask = torch.nonzero(target.data == self.padding_idx)\n",
    "        if mask.dim() > 0:\n",
    "            true_dist.index_fill_(0, mask.squeeze(), 0.0)\n",
    "        self.true_dist = true_dist\n",
    "        return self.criterion(x, Variable(true_dist, requires_grad=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Here we can see an example of how the mass is distributed to the words based on confidence. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b060c338cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Example of label smoothing.\n",
    "crit = LabelSmoothing(5, 0, 0.4)\n",
    "predict = torch.FloatTensor([[0, 0.2, 0.7, 0.1, 0],\n",
    "                             [0, 0.2, 0.7, 0.1, 0], \n",
    "                             [0, 0.2, 0.7, 0.1, 0]])\n",
    "v = crit(Variable(predict.log()), \n",
    "         Variable(torch.LongTensor([2, 1, 0])))\n",
    "\n",
    "# Show the target distributions expected by the system.\n",
    "plt.imshow(crit.true_dist)\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Label smoothing actually starts to penalize the model if it gets very confident about a given choice. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b060c321128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crit = LabelSmoothing(5, 0, 0.1)\n",
    "def loss(x):\n",
    "    d = x + 3 * 1\n",
    "    predict = torch.FloatTensor([[0, x / d, 1 / d, 1 / d, 1 / d],\n",
    "                                 ])\n",
    "    #print(predict)\n",
    "    return crit(Variable(predict.log()),\n",
    "                 Variable(torch.LongTensor([1]))).data[0]\n",
    "plt.plot(np.arange(1, 100), [loss(x) for x in range(1, 100)])\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A First  Example\n",
    "\n",
    "> We can begin by trying out a simple copy-task. Given a random set of input symbols from a small vocabulary, the goal is to generate back those same symbols. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthetic Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def data_gen(V, batch, nbatches):\n",
    "    \"Generate random data for a src-tgt copy task.\"\n",
    "    for i in range(nbatches):\n",
    "        data = torch.from_numpy(np.random.randint(1, V, size=(batch, 10)))\n",
    "        data[:, 0] = 1\n",
    "        src = Variable(data, requires_grad=False)\n",
    "        tgt = Variable(data, requires_grad=False)\n",
    "        yield Batch(src, tgt, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loss Computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SimpleLossCompute:\n",
    "    \"A simple loss compute and train function.\"\n",
    "    def __init__(self, generator, criterion, opt=None):\n",
    "        self.generator = generator\n",
    "        self.criterion = criterion\n",
    "        self.opt = opt\n",
    "        \n",
    "    def __call__(self, x, y, norm):\n",
    "        x = self.generator(x)\n",
    "        loss = self.criterion(x.contiguous().view(-1, x.size(-1)), \n",
    "                              y.contiguous().view(-1)) / norm\n",
    "        loss.backward()\n",
    "        if self.opt is not None:\n",
    "            self.opt.step()\n",
    "            self.opt.optimizer.zero_grad()\n",
    "        return loss.data[0] * norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Greedy Decoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch Step: 1 Loss: 3.023465 Tokens per Sec: 403.074173\n",
      "Epoch Step: 1 Loss: 1.920030 Tokens per Sec: 641.689380\n",
      "1.9274832487106324\n",
      "Epoch Step: 1 Loss: 1.940011 Tokens per Sec: 432.003378\n",
      "Epoch Step: 1 Loss: 1.699767 Tokens per Sec: 641.979665\n",
      "1.657595729827881\n",
      "Epoch Step: 1 Loss: 1.860276 Tokens per Sec: 433.320240\n",
      "Epoch Step: 1 Loss: 1.546011 Tokens per Sec: 640.537198\n",
      "1.4888023376464843\n",
      "Epoch Step: 1 Loss: 1.682198 Tokens per Sec: 432.092305\n",
      "Epoch Step: 1 Loss: 1.313169 Tokens per Sec: 639.441857\n",
      "1.3485562801361084\n",
      "Epoch Step: 1 Loss: 1.278768 Tokens per Sec: 433.568756\n",
      "Epoch Step: 1 Loss: 1.062384 Tokens per Sec: 642.542067\n",
      "0.9853351473808288\n",
      "Epoch Step: 1 Loss: 1.269471 Tokens per Sec: 433.388727\n",
      "Epoch Step: 1 Loss: 0.590709 Tokens per Sec: 642.862135\n",
      "0.5686767101287842\n",
      "Epoch Step: 1 Loss: 0.997076 Tokens per Sec: 433.009746\n",
      "Epoch Step: 1 Loss: 0.343118 Tokens per Sec: 642.288427\n",
      "0.34273059368133546\n",
      "Epoch Step: 1 Loss: 0.459483 Tokens per Sec: 434.594030\n",
      "Epoch Step: 1 Loss: 0.290385 Tokens per Sec: 642.519464\n",
      "0.2612409472465515\n",
      "Epoch Step: 1 Loss: 1.031042 Tokens per Sec: 434.557008\n",
      "Epoch Step: 1 Loss: 0.437069 Tokens per Sec: 643.630322\n",
      "0.4323212027549744\n",
      "Epoch Step: 1 Loss: 0.617165 Tokens per Sec: 436.652626\n",
      "Epoch Step: 1 Loss: 0.258793 Tokens per Sec: 644.372296\n",
      "0.27331129014492034\n"
     ]
    }
   ],
   "source": [
    "# Train the simple copy task.\n",
    "V = 11\n",
    "criterion = LabelSmoothing(size=V, padding_idx=0, smoothing=0.0)\n",
    "model = make_model(V, V, N=2)\n",
    "model_opt = NoamOpt(model.src_embed[0].d_model, 1, 400,\n",
    "        torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))\n",
    "\n",
    "for epoch in range(10):\n",
    "    model.train()\n",
    "    run_epoch(data_gen(V, 30, 20), model, \n",
    "              SimpleLossCompute(model.generator, criterion, model_opt))\n",
    "    model.eval()\n",
    "    print(run_epoch(data_gen(V, 30, 5), model, \n",
    "                    SimpleLossCompute(model.generator, criterion, None)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This code predicts a translation using greedy decoding for simplicity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    1     2     3     4     5     6     7     8     9    10\n",
      "[torch.LongTensor of size 1x10]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def greedy_decode(model, src, src_mask, max_len, start_symbol):\n",
    "    memory = model.encode(src, src_mask)\n",
    "    ys = torch.ones(1, 1).fill_(start_symbol).type_as(src.data)\n",
    "    for i in range(max_len-1):\n",
    "        out = model.decode(memory, src_mask, \n",
    "                           Variable(ys), \n",
    "                           Variable(subsequent_mask(ys.size(1))\n",
    "                                    .type_as(src.data)))\n",
    "        prob = model.generator(out[:, -1])\n",
    "        _, next_word = torch.max(prob, dim = 1)\n",
    "        next_word = next_word.data[0]\n",
    "        ys = torch.cat([ys, \n",
    "                        torch.ones(1, 1).type_as(src.data).fill_(next_word)], dim=1)\n",
    "    return ys\n",
    "\n",
    "model.eval()\n",
    "src = Variable(torch.LongTensor([[1,2,3,4,5,6,7,8,9,10]]) )\n",
    "src_mask = Variable(torch.ones(1, 1, 10) )\n",
    "print(greedy_decode(model, src, src_mask, max_len=10, start_symbol=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Real World Example\n",
    "\n",
    "> Now we consider a real-world example using the IWSLT German-English Translation task. This task is much smaller than the WMT task considered in the paper, but it illustrates the whole system. We also show how to use multi-gpu processing to make it really fast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#!pip install torchtext spacy\n",
    "#!python -m spacy download en\n",
    "#!python -m spacy download de"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading\n",
    "> We will load the dataset using torchtext and spacy for tokenization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For data loading.\n",
    "from torchtext import data, datasets\n",
    "\n",
    "if True:\n",
    "    import spacy\n",
    "    spacy_de = spacy.load('de')\n",
    "    spacy_en = spacy.load('en')\n",
    "\n",
    "    def tokenize_de(text):\n",
    "        return [tok.text for tok in spacy_de.tokenizer(text)]\n",
    "\n",
    "    def tokenize_en(text):\n",
    "        return [tok.text for tok in spacy_en.tokenizer(text)]\n",
    "\n",
    "    BOS_WORD = '<s>'\n",
    "    EOS_WORD = '</s>'\n",
    "    BLANK_WORD = \"<blank>\"\n",
    "    SRC = data.Field(tokenize=tokenize_de, pad_token=BLANK_WORD)\n",
    "    TGT = data.Field(tokenize=tokenize_en, init_token = BOS_WORD, \n",
    "                     eos_token = EOS_WORD, pad_token=BLANK_WORD)\n",
    "\n",
    "    MAX_LEN = 100\n",
    "    train, val, test = datasets.IWSLT.splits(\n",
    "        exts=('.de', '.en'), fields=(SRC, TGT), \n",
    "        filter_pred=lambda x: len(vars(x)['src']) <= MAX_LEN and \n",
    "            len(vars(x)['trg']) <= MAX_LEN)\n",
    "    MIN_FREQ = 2\n",
    "    SRC.build_vocab(train.src, min_freq=MIN_FREQ)\n",
    "    TGT.build_vocab(train.trg, min_freq=MIN_FREQ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Batching matters a ton for speed. We want to have very evenly divided batches, with absolutely minimal padding. To do this we have to hack a bit around the default torchtext batching. This code patches their default batching to make sure we search over enough sentences to find tight batches. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MyIterator(data.Iterator):\n",
    "    def create_batches(self):\n",
    "        if self.train:\n",
    "            def pool(d, random_shuffler):\n",
    "                for p in data.batch(d, self.batch_size * 100):\n",
    "                    p_batch = data.batch(\n",
    "                        sorted(p, key=self.sort_key),\n",
    "                        self.batch_size, self.batch_size_fn)\n",
    "                    for b in random_shuffler(list(p_batch)):\n",
    "                        yield b\n",
    "            self.batches = pool(self.data(), self.random_shuffler)\n",
    "            \n",
    "        else:\n",
    "            self.batches = []\n",
    "            for b in data.batch(self.data(), self.batch_size,\n",
    "                                          self.batch_size_fn):\n",
    "                self.batches.append(sorted(b, key=self.sort_key))\n",
    "\n",
    "def rebatch(pad_idx, batch):\n",
    "    \"Fix order in torchtext to match ours\"\n",
    "    src, trg = batch.src.transpose(0, 1), batch.trg.transpose(0, 1)\n",
    "    return Batch(src, trg, pad_idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Multi-GPU Training\n",
    "\n",
    "> Finally to really target fast training, we will use multi-gpu. This code implements multi-gpu word generation. It is not specific to transformer so I won't go into too much detail. The idea is to split up word generation at training time into chunks to be processed in parallel across many different gpus. We do this using pytorch parallel primitives:\n",
    "\n",
    "* replicate - split modules onto different gpus.\n",
    "* scatter - split batches onto different gpus\n",
    "* parallel_apply - apply module to batches on different gpus\n",
    "* gather - pull scattered data back onto one gpu. \n",
    "* nn.DataParallel - a special module wrapper that calls these all before evaluating. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Skip if not interested in multigpu.\n",
    "class MultiGPULossCompute:\n",
    "    \"A multi-gpu loss compute and train function.\"\n",
    "    def __init__(self, generator, criterion, devices, opt=None, chunk_size=5):\n",
    "        # Send out to different gpus.\n",
    "        self.generator = generator\n",
    "        self.criterion = nn.parallel.replicate(criterion, \n",
    "                                               devices=devices)\n",
    "        self.opt = opt\n",
    "        self.devices = devices\n",
    "        self.chunk_size = chunk_size\n",
    "        \n",
    "    def __call__(self, out, targets, normalize):\n",
    "        total = 0.0\n",
    "        generator = nn.parallel.replicate(self.generator, \n",
    "                                                devices=self.devices)\n",
    "        out_scatter = nn.parallel.scatter(out, \n",
    "                                          target_gpus=self.devices)\n",
    "        out_grad = [[] for _ in out_scatter]\n",
    "        targets = nn.parallel.scatter(targets, \n",
    "                                      target_gpus=self.devices)\n",
    "\n",
    "        # Divide generating into chunks.\n",
    "        chunk_size = self.chunk_size\n",
    "        for i in range(0, out_scatter[0].size(1), chunk_size):\n",
    "            # Predict distributions\n",
    "            out_column = [[Variable(o[:, i:i+chunk_size].data, \n",
    "                                    requires_grad=self.opt is not None)] \n",
    "                           for o in out_scatter]\n",
    "            gen = nn.parallel.parallel_apply(generator, out_column)\n",
    "\n",
    "            # Compute loss. \n",
    "            y = [(g.contiguous().view(-1, g.size(-1)), \n",
    "                  t[:, i:i+chunk_size].contiguous().view(-1)) \n",
    "                 for g, t in zip(gen, targets)]\n",
    "            loss = nn.parallel.parallel_apply(self.criterion, y)\n",
    "\n",
    "            # Sum and normalize loss\n",
    "            l = nn.parallel.gather(loss, \n",
    "                                   target_device=self.devices[0])\n",
    "            l = l.sum()[0] / normalize\n",
    "            total += l.data[0]\n",
    "\n",
    "            # Backprop loss to output of transformer\n",
    "            if self.opt is not None:\n",
    "                l.backward()\n",
    "                for j, l in enumerate(loss):\n",
    "                    out_grad[j].append(out_column[j][0].grad.data.clone())\n",
    "\n",
    "        # Backprop all loss through transformer.            \n",
    "        if self.opt is not None:\n",
    "            out_grad = [Variable(torch.cat(og, dim=1)) for og in out_grad]\n",
    "            o1 = out\n",
    "            o2 = nn.parallel.gather(out_grad, \n",
    "                                    target_device=self.devices[0])\n",
    "            o1.backward(gradient=o2)\n",
    "            self.opt.step()\n",
    "            self.opt.optimizer.zero_grad()\n",
    "        return total * normalize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Now we create our model, criterion, optimizer, data iterators, and paralelization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GPUs to use\n",
    "devices = [0, 1, 2, 3]\n",
    "if True:\n",
    "    pad_idx = TGT.vocab.stoi[\"<blank>\"]\n",
    "    model = make_model(len(SRC.vocab), len(TGT.vocab), N=6)\n",
    "    model.cuda()\n",
    "    criterion = LabelSmoothing(size=len(TGT.vocab), padding_idx=pad_idx, smoothing=0.1)\n",
    "    criterion.cuda()\n",
    "    BATCH_SIZE = 12000\n",
    "    train_iter = MyIterator(train, batch_size=BATCH_SIZE, device=0,\n",
    "                            repeat=False, sort_key=lambda x: (len(x.src), len(x.trg)),\n",
    "                            batch_size_fn=batch_size_fn, train=True)\n",
    "    valid_iter = MyIterator(val, batch_size=BATCH_SIZE, device=0,\n",
    "                            repeat=False, sort_key=lambda x: (len(x.src), len(x.trg)),\n",
    "                            batch_size_fn=batch_size_fn, train=False)\n",
    "    model_par = nn.DataParallel(model, device_ids=devices)\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Now we train the model. I will play with the warmup steps a bit, but everything else uses the default parameters.  On an AWS p3.8xlarge with 4 Tesla V100s, this runs at ~27,000 tokens per second with a batch size of 12,000 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#!wget https://s3.amazonaws.com/opennmt-models/iwslt.pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if False:\n",
    "    model_opt = NoamOpt(model.src_embed[0].d_model, 1, 2000,\n",
    "            torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))\n",
    "    for epoch in range(10):\n",
    "        model_par.train()\n",
    "        run_epoch((rebatch(pad_idx, b) for b in train_iter), \n",
    "                  model_par, \n",
    "                  MultiGPULossCompute(model.generator, criterion, \n",
    "                                      devices=devices, opt=model_opt))\n",
    "        model_par.eval()\n",
    "        loss = run_epoch((rebatch(pad_idx, b) for b in valid_iter), \n",
    "                          model_par, \n",
    "                          MultiGPULossCompute(model.generator, criterion, \n",
    "                          devices=devices, opt=None))\n",
    "        print(loss)\n",
    "else:\n",
    "    model = torch.load(\"iwslt.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Once trained we can decode the model to produce a set of translations. Here we simply translate the first sentence in the validation set. This dataset is pretty small so the translations with greedy search are reasonably accurate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Translation:\t<unk> <unk> . In my language , that means , thank you very much . \n",
      "Target:\t<unk> <unk> . It means in my language , thank you very much . \n"
     ]
    }
   ],
   "source": [
    "for i, batch in enumerate(valid_iter):\n",
    "    src = batch.src.transpose(0, 1)[:1]\n",
    "    src_mask = (src != SRC.vocab.stoi[\"<blank>\"]).unsqueeze(-2)\n",
    "    out = greedy_decode(model, src, src_mask, \n",
    "                        max_len=60, start_symbol=TGT.vocab.stoi[\"<s>\"])\n",
    "    print(\"Translation:\", end=\"\\t\")\n",
    "    for i in range(1, out.size(1)):\n",
    "        sym = TGT.vocab.itos[out[0, i]]\n",
    "        if sym == \"</s>\": break\n",
    "        print(sym, end =\" \")\n",
    "    print()\n",
    "    print(\"Target:\", end=\"\\t\")\n",
    "    for i in range(1, batch.trg.size(0)):\n",
    "        sym = TGT.vocab.itos[batch.trg.data[i, 0]]\n",
    "        if sym == \"</s>\": break\n",
    "        print(sym, end =\" \")\n",
    "    print()\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Additional Components: BPE, Search, Averaging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> So this mostly covers the transformer model itself. There are four aspects that we didn't cover explicitly. We also have all these additional features implemented in [OpenNMT-py](https://github.com/opennmt/opennmt-py).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 1) BPE/ Word-piece: We can use a library to first preprocess the data into subword units. See Rico Sennrich's [subword-nmt](https://github.com/rsennrich/subword-nmt) implementation. These models will transform the training data to look like this:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "▁Die ▁Protokoll datei ▁kann ▁ heimlich ▁per ▁E - Mail ▁oder ▁FTP ▁an ▁einen ▁bestimmte n ▁Empfänger ▁gesendet ▁werden ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 2) Shared Embeddings: When using BPE with shared vocabulary we can share the same weight vectors between the source / target / generator. See the [(cite)](https://arxiv.org/abs/1608.05859) for details. To add this to the model simply do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if False:\n",
    "    model.src_embed[0].lut.weight = model.tgt_embeddings[0].lut.weight\n",
    "    model.generator.lut.weight = model.tgt_embed[0].lut.weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 3) Beam Search: This is a bit too complicated to cover here. See the [OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py/blob/master/onmt/translate/Beam.py) for a pytorch implementation.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 4) Model Averaging: The paper averages the last k checkpoints to create an ensembling effect. We can do this after the fact if we have a bunch of models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def average(model, models):\n",
    "    \"Average models into model\"\n",
    "    for ps in zip(*[m.params() for m in [model] + models]):\n",
    "        p[0].copy_(torch.sum(*ps[1:]) / len(ps[1:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "\n",
    "On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big)\n",
    "in Table 2) outperforms the best previously reported models (including ensembles) by more than 2.0\n",
    "BLEU, establishing a new state-of-the-art BLEU score of 28.4. The configuration of this model is\n",
    "listed in the bottom line of Table 3. Training took 3.5 days on 8 P100 GPUs. Even our base model\n",
    "surpasses all previously published models and ensembles, at a fraction of the training cost of any of\n",
    "the competitive models.\n",
    "\n",
    "On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.0,\n",
    "outperforming all of the previously published single models, at less than 1/4 the training cost of the\n",
    "previous state-of-the-art model. The Transformer (big) model trained for English-to-French used\n",
    "dropout rate Pdrop = 0.1, instead of 0.3.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"images/results.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The code we have written here is a version of the base model. There are fully trained version of this system available here  [(Example Models)](http://opennmt.net/Models-py/).\n",
    ">\n",
    "> With the addtional extensions in the last section, the OpenNMT-py replication gets to 26.9 on EN-DE WMT. Here I have loaded in those parameters to our reimplemenation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!wget https://s3.amazonaws.com/opennmt-models/en-de-model.pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model, SRC, TGT = torch.load(\"en-de-model.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Translation:\t<s> ▁Die ▁Protokoll datei ▁kann ▁ heimlich ▁per ▁E - Mail ▁oder ▁FTP ▁an ▁einen ▁bestimmte n ▁Empfänger ▁gesendet ▁werden . \n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "sent = \"▁The ▁log ▁file ▁can ▁be ▁sent ▁secret ly ▁with ▁email ▁or ▁FTP ▁to ▁a ▁specified ▁receiver\".split()\n",
    "src = torch.LongTensor([[SRC.stoi[w] for w in sent]])\n",
    "src = Variable(src)\n",
    "src_mask = (src != SRC.stoi[\"<blank>\"]).unsqueeze(-2)\n",
    "out = greedy_decode(model, src, src_mask, \n",
    "                    max_len=60, start_symbol=TGT.stoi[\"<s>\"])\n",
    "print(\"Translation:\", end=\"\\t\")\n",
    "trans = \"<s> \"\n",
    "for i in range(1, out.size(1)):\n",
    "    sym = TGT.itos[out[0, i]]\n",
    "    if sym == \"</s>\": break\n",
    "    trans += sym + \" \"\n",
    "print(trans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attention Visualization\n",
    "\n",
    "> Even with a greedy decoder the translation looks pretty good. We can further visualize it to see what is happening at each layer of the attention "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoder Layer 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b063c06df60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoder Layer 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b0679e90b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoder Layer 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b067a11a588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoder Self Layer 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b063c06ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoder Src Layer 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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tYTYCnAd8D7g2yfGd6SuAncCmJMuAN/epPkmSJEmSJPXZfGuYUVVjVXUBcA3whSSnAa8Gjgc2At8B/hUY7V+VkiRJkiRJ6pcF/S5gplTVGRMevwR4Sdekn5sQeX/XshfvZ33ndN2/lgmv5f4ykiRJkiRJmv3mdMMsySrG9xSbjHOr6o7prKdXBh/yi42zxaU9rGTyxqpa5b+78ebG2YE0P9Vc27qlw95gs6+JavHZGqn+7eA7NDDYODvGWOOsWyLp4HLG/RvlhsdGWo3bZlt2z87NjbOj1Xx7MjjQ/ACS0bHm40rzwWvr1EMvtD+74c+Hmm8Tbt694dALHcCRQ0sbZ1cdcWzj7F0772uc3bl3T+Os1EtzumEGDAFrprCsJEmSJEkzpk2zTFL/zOmGWVWtxytZSpIkSZIkqYfm3Un/JUmSJEmSpIOxYSZJkiRJkiR1sWEmSZIkSZIkdbFhJkmSJEmSJHWxYTZLJFmZZHWS1Rs3eRUVSZIkSZKkfrFhNntcCKwF1r7zPR/ody2SJEmSJEnzlg2z2eMyYA2w5ref/Yx+1yJJkiRJkjRvLeh3ARpXVRuBjQDDt3+3z9VIkiRJkiTNX+5hJkmSJEmSJHWxYSZJkiRJkiR1sWEmSZIkSZIkdbFhJkmSJEmSJHXxpP8tJFkFXDPJxc+tqjumsx5JkiRJkiS1Z8OsnSFgzRSWnZTv/OyrmlUDpHESBgcGG2eXL1rSYmT4yeWnN87evXdr4+zCgeYfge9svLlxVpor9v6fP26Uqx7XMRc84+SfbZz923u+0Ti7Z2Rv46w0V4x98Z8b5dp8zwPsruafr8ULJv2r33+xY++extk2zjrypMbZm7bc2cNKpNnp6kXNPptf2nB9q3EXDjbflg21+Btvw67NjbNHL17WOHv1sp9pnH3Spi80zkoT2TBroarW065HJUmSJEmSpFnGc5hJkiRJkiRJXWZNwyzJ+iTPbJg9L8l1vRo/yWlJtic5eRK585Pc0GZsSZIkSZIkzR6zpmHWRlVdVVUP7uH6bq2qZVX1w16tU5IkSZIkSXPDYdEwkyRJkiRJknpltjXMTktyTedwyO8leeS+GUku6EzbkuRbSR7XNe9HDotMcm2SP0ny90m2JbkxyblJfqGzjq2decv3V0SSM5JUklO6pv1qkq8n2ZzkriRvmJB5cZLbk2xK8pdJml+ORJIkSZIkSX0z2xpmzwFeDBwJfAa4EsabZcDLgPOAo4FXAB9JcvZB1vUbwJuAo4APAe8Dng88GjgDWNMZ65CSPKFTy8XAscBq4FNdi5wOnAD8GPDTwFOBp09m3V1jrEyyOsnqzaPDU4lKkiRJkiSph2Zbw+wvq+r7VTUKXA6cneRI4CLgdVV1XVWNVdUngc9x8KbU1VX11c663g+cBLylqu6rqvuAjwMPn2RdFwJ/UVUfr6qRqtpaVV/smr8LeHVV7amqG4BrprDu7jHWAmuv3nHTFKOSJEmSJEnqldnWMLuz6/6Ozr/LgTOBd3QOh9ycZDPwGGDVJNe18wDT9ntI5n6cAaw7yPx7Oo25fXZMYd37XMb4Xm9r/tcRZ00xKkmSJEmSpF6ZbQ2zA7kFeE5VHdV1W1ZVvzVD468H7jedA1TVxqpaV1XrjhpcOJ1DSZIkSZIk6SDmSsPsbcDFSR6ScUuSPCrJ/Wdo/HcAv5XkCUkWJFmR5FEzNLYkSZIkSZJm0JxomFXVu4BLgPcAm4BbgVcBQzM0/ieA5wJvBO5j/Fxjj5+JsSVJkiRJkjSzFvS7gH2q6owJj9cD6Xp8JZ2rZu4newVwRdfjcw62rs60iw80/gGWvxq4+lBjd6adv786JUmSJEmSNPvNiT3MJEmSJEmSpJkya/Yw0386/cc3NQ/f0zw6MjZ66IUOYNOu7c0HBj6/6/uNs4MDzfu+o2NjjbNvO+ExjbO/d/fnGmelmbTgGc9tFvyzr/e2kBkyVs23CR+999uNs222RdJ8MLLu9ka50RafaYBlCxc3zi5pcRGnbcO7GmerqnF22YLmz3fBwGDjbJvfQaWZdH+W9GXcRYPNz0R0967mf1smOfRCB7B7ZLhx9qalzdsUKxYtbZwF2LpnZ6u8Di/uYSZJkiRJkiR1sWEmSZIkSZIkdbFhJkmSJEmSJHWxYSZJkiRJkiR1sWE2zZI0P+OrJEmSJEmSZpwNswmSrE/y6iRfTLI9ydeT/PSEZS5I8r0kW5J8K8njuuZdnOSzSd6a5G7gYzP+JCRJkiRJktSYDbP9eyFwEXAM8LfAJ5OsgPFmGfAy4DzgaOAVwEeSnN2VfzRwJ3Aq8JTJDJhkZZLVSVbfN7y3Z09EkiRJkiRJU2PDbP/eXVXfqKph4M3ALuBJnXkXAa+rquuqaqyqPgl8Dnh6V/6Wqrq0qoarauckx7wQWAusvfy2O3r0NCRJkiRJkjRVNsz2b/2+O1VVwK3AKZ1JZwLvSLJ53w14DLCqK39LgzEvA9YAa5536qpDLStJkiRJkqRpsqDfBcxSZ+y7kyTAacDtnUm3AK+pqg8fJD821QGraiOwEWDDL/73qcYlSZIkSZLUI+5htn/PSfKwJEPAHwBLgU905r0NuDjJQzJuSZJHJbl/36qVJEmSJElSz7iH2f79FfBnwEMYP6/YE6tqC0BVvSvJMPAexg/P3At8E/jffapVkiRJkiRJPWTDbP9urKrXHmhmVV0JXHmAeRdPV1GSJEmSJEmafvOiYZZkFXDNJBf3jPuSJEmSJEnz2LxomAFDjF+BcjKaXOGyp37hu3sbZ6uHdcwVo2NTvsZCT7xp+7caZ7f95XmNs8tfcFXjrDRVOfL4fpcwo9psT3YM7+5hJZM3NNj8q3ys2m0/+7X91fwzcMyyRrkjhha3GrfNZ2SsxW9l4xdpb2YgzU9RfOO2Oxtn27xWbbZjAHtHR1rlpcn6/Q/9cqPcax59batxR8ZGG2dHW2THr38389mXbPiXxtm2v9u02R65LTr8zIuGWVWtB5p/YiVJkiRJkjRveJVMSZIkSZIkqYsNM0mSJEmSJKmLDTNJkiRJkiSpiw2zHklybZI9SbZPuD2o37VJkiRJkiRp8myY9dbrq2rZhNt3+12UJEmSJEmSJs+G2SyRZGWS1UlWj4zt7Xc5kiRJkiRJ85YNs9njQmAtsPbenXf1uxZJkiRJkqR5y4ZZb70iyebu2xSylwFrgDXHLj1xmsqTJEmSJEnSodgw6603VNVR3bfJBqtqY1Wtq6p1CwaGprNGSZIkSZIkHYQNM0mSJEmSJKmLDTNJkiRJkiSpiw2z3npVku0Tbk/qd1GSJEmSJEmavAX9LuBwUVXn9LsGSZIkSZIktWfD7CCSrAKumeTi51bVHb0Y94atP+zFaqYsLbLVsyrmjrt3TOUiqD8qD3104+wRC/+ucRZgx/DuVnnNL7/ymNc1yrXZnrTVZnu0dGhR4+yuvXsaZwcHBhtnh1pk77jgJxtnAY555zcbZ+fj94aaGzjp2Ea5Tbu/0WrcJQsWNs4uXdB8e5I034pWi0/X8OhI42ybmsdqrHEWYEGL7eDI2GirsTW/1L09+XNvytq8xwcHmh9UtrfFNqGq+bao1TawxbgAg2lxEN5g8/ZKm9da08eG2cENAWumsKwkSZIkSZLmOBtmB1FV6+nvjhKSJEmSJEmaYZ70X5IkSZIkSepiw0ySJEmSJEnqYsNMkiRJkiRJ6mLDTJIkSZIkSeriSf9niSQrgZUASxafQtL80sGSJEmSJElqzj3MZo8LgbXA2r0j2/pdiyRJkiRJ0rxlw2z2uAxYA6wZWrC837VIkiRJkiTNWzbMpkGS85Js33ebTKaqNlbVuqpa5+GYkiRJkiRJ/WPDbBpU1VVVtWzfrd/1SJIkSZIkafJsmEmSJEmSJEldbJhJkiRJkiRJXWyYSZIkSZIkSV0W9LsA/VcPPOr0xtlv3ntD42w1TkJaZNuO3S9b3/6rjbMrH/mixtnhkb2NswCLFgw1zu5pObbmnuMGljTKJc23CmPVvy3Crr17GmcHBpr/H9RYjTXOPvioMxtn//IjRzbOAvz3E36icfbLG9c2zrotmn++/OatjXKDLT6XAMcsXtE4e/yi5p+vzXt2NM7uHm3++Th+afOa79m5pXF24WC7P0mGR0caZ1csWto4u3XPzsZZzU2vedHXGuXa/q20uMXv77tbfGcODjS/GN0RQ4saZ49c2Pw04Pfubr4tAnjYUWc1zt6+577G2WMXNv+++dqGdY2zOjj3MJMkSZIkSZK62DCTJEmSJEmSutgwkyRJkiRJkrrYMOuRJJ9K8tKux5XkUf2sSZIkSZIkSVM37xtmSa7tNLf+14Tpj+hMXz+Z9VTVE6rqkmkpUpIkSZIkSTNm3jfMOv4NuGDCtAs60yVJkiRJkjSP2DAb9xHgoUnOAkiyHHgK8J59CyR5epLrkmxNcmeSv0xyRNf8a5O8smkBSVYmWZ1k9chY80v/SpIkSZIkqR0bZuN2A1cBz+08/nXg88CdXctsAZ4BHAX8fOfWuEG2HxcCa4G1d++4o4erlSRJkiRJ0lTYMPtP7wKenWQB8PzO4/9QVZ+qqu9X1VhV3QC8Ezi3h+NfBqwB1pxwxKoerlaSJEmSJElTYcOso6q+B9wCvAo4Hvin7vlJfjHJF5JsSLIVeDNwXA/H31hV66pq3YKBoV6tVpIkSZIkSVNkw+xH/RXjDbO/rqrRfROTLAQ+CvwNcFpVrQBeBqQvVUqSJEmSJGnaLOh3AbPMB4HbgG9MmL4QWARsqqpdSR4AvGimi5MkSZIkSdL0cw+zLlW1u6r+uao2TZi+Hfgt4JIk24F3AB/oR42SJEmSJEmaXvN+D7OqOucg894PvL9z/11MuBAA8LoDraeqPFxTkiRJkiRpDpr3DbPZaDBzb8e/aplfvGBh4+zukeGWozc02Pzjs2dkbw8LmZqRsdFDL3QAbbrAbd8j6o8P3fW1Rrmx6t9PvF//WzE6NtY4u2BgsHH2+1tvbZxlRfMowKaRHY2zt557euPsCf/3hsZZzU0L0/y7q42te5u/x6vFN9/u0ea/J7T5PXJPi3GPGFrUOAuwt8XvJ9Wn75zTVhzfOHvr1nt6WIlmymmjzb6v33bCY/j9DZ9vPG6b3zHabIvGqvm4bewZa/73XZu/dQBu2X1v4+ye0eZ1Dy08qnH25GXHNM7+cPt9jbPzwdzrzEiSJEk6bLRplklzQZtmmaT+sWEmSZIkSZIkdbFhJkmSJEmSJHWxYXYISU5JUknO6HctkiRJkiRJmn42zCRJkiRJkqQuNsymWZLml3+UJEmSJEnSjJt3DbMkK5O8N8ldnduVSY7pmn9ikn9IsiXJOuCX9rOOC5J8r7PMt5I8rmvexUk+m+StSe4GPjaFulYnWb13rPklvSVJkiRJktTOvGuYAVcBRwM/3rkdC7xvwvxR4DTg0cD53eEkFwAvA87rrOcVwEeSnN212KOBO4FTgadMsq4LgbXA2ru23z6lJyRJkiRJkqTeWdDvAmZSkpOBxwOrq2pTZ9pLgOuTnMR4A/GxwNlVtQXYkuS1wKe7VnMR8Lqquq7z+JNJPgc8HfijzrRbqurSzv3hSZZ3GfABgBOXnbK20ROUJEmSJElSa/NtD7NTO//e3DXtxq55p3Tu39I1v3tZgDOBdyTZvO8GPAZY1bXMLUxRVW2sqnVVtW5oYGiqcUmSJEmSJPXIvNrDDLit8+8ZwA2d+2d1zRvs3D+d/2yknTFhHbcAr6mqDx9knLFWVUqSJEmSJKlv5tUeZlX1Q8YPr7w0yVFJjgYuBT5VVXdW1e3AtcAlSVYkOQF49YTVvA24OMlDMm5Jkkcluf9MPhdJkiRJkiRNj3nVMOt4JrCN8RPsXw9sBn6za/4zgEWM73H2BeC93eGqehdwCfAeYBNwK/AqwOMoJUmSJEmSDgPz7ZBMqmoD402zA82/E3jShMmXT1jmSuDKA+QvblmiJEmSJEmS+uiwaJglWQVcM8nFz62qO6aznrZ2jO7udwkzbvfIZC8mOnvs+tg3+11CI2NKKeGDAAAgAElEQVRjzU+xd8Kyoxtn77f0pMbZL9zzg8ZZtXPnE8869EL7ccb/ve3QCx3Err17GmfHqhpnFwwMHnqhA1g42Pwrtc02cPvwrsbZr967lp885szG+dt2bGicPeWfRxpnB5LG2SVDixpndwzPv+/n2WLJgmbvl88f8xAedW/z7+uR0dHG2a21o3G2zXu8jW0ttifQbhs6Mtb8tW5jqEXNe0abb7vvf/Sph17oIK7f1O57Vs08km2Ncl8+7mE84u6vNx53b4vPx2iL3/2rxe9UbbYn24Z3tdqe7B1t/jvGpt3NfsYAO0f68/vr0gWLG2dPX3ECP7bk+Mb5z9793cbZueCwaJgxfjjkmiksK0mapdo0yzQ1bZpl0lzQplmmqZmLzbK5ymbZ3NOmWTYf9atZNh/ZLDu4w6JhVlXrgf78V5wkSZIkSZIOK/PxpP+SJEmSJEnSAdkwkyRJkiRJkrocFodk9kKSa4GfA/Z2Tf4bYIT/vKrmALAE6D6L6wsYPy/aXwM7O9PuAz4CvKyqPBmPJEmSJEnSHGLD7Ee9vqr+aD/TXwiQ5FHAF6pqWffMJOcDN1XV2Z3HDwE+DWwFXj2tFUuSJEmSJKmnPCRzGlTVt4F/AR7a71okSZIkSZI0NTbMeizjHgr8d+BrU8itTLI6yeqRsb2HDkiSJEmSJGla2DD7Ua9Isrnr9rNTyJ6ZZDPj5y+7mvFzmr1pCvkLgbXA2o0775pCTJIkSZIkSb3kOcx+1BsOcA6zybh53znMGroM+ADAyqUnrm2xHkmSJEmSJLXgHmazRFVtrKp1VbVuwcBQv8uRJEmSJEmat2yYSZIkSZIkSV1smEmSJEmSJEldPIdZR1WdM4llvghkP9OvAK7oeVGSJEmSJEmacYd1wyzJKuCaSS5+blXdMZ31SJIkSZIkafY7rBtmwBCwZgrLzgpfvuDUxtkj33xrDyvRwZzwmRv6XUIj1SJ71/ZNfck+9+RHNs6++4f/2jgrWPzsX26UG/7k2xuPOVZt3qXtjI6NNs7ubJH9L7suT8HAQPOzK3znvptbjAzV4me1dOHixtndNdY4Ozw60jir/vkGyxrlqtW3Hoy2eK8tHVzUOLt9eHfj7GCLbcJg+nO2lr0tP5dttkVbh3c1zh4x1PxnPJTBxtnHnvCgxtnP3v3dxlnB79fOvozbZlvUZpswNtp83MULFjbOHrlwaePsUMsL6G3Ytblxts22aO9Y8+3gjpHm27HH5djG2euWLG+cBdi4a1ur/Ew4rBtmVbWedn+HSJIkSZIkaZ7xpP+SJEmSJElSFxtmkiRJkiRJUhcbZpIkSZIkSVIXG2aSJEmSJElSFxtmk5TkoiTXJ9mW5NYkf5z85+VtklSS307ytc4yX0ly/37WLEmSJEmSpKmzYTZ5twNPAFYATwaeAzxvwjLnA08BjgVuAy6b7MqTrEyyOsnqjTv39KRgSZIkSZIkTZ0Ns0mqqr+rqptr3LeA9wHnTljsLVV1a1XtAa4AHj6FIS4E1gJr/+JrN/SkZkmSJEmSJE2dDbNJSvLrncMtNybZAvwOcNyExe7sur8DWD6FIS4D1gBrXvjTZ7crVpIkSZIkSY3ZMJuEJKcC7wf+CDipqo4E3gGkV2NU1caqWldV61YuXdSr1UqSJEmSJGmKbJhNzjLGX6sNwN4kPwv8Rn9LkiRJkiRJ0nRY0O8C5oKq+rckrwE+BiwEPgd8EHhIXwuTJEmSJElSz9kwm6Sqeh3wuoPMz4TH1+LrK0mSJEmSNOfM64ZOklXANZNc/NyqumM665EkSZIkSVL/zeuGGTDE+JUpJ7vsjPilv944U0NJc8KfvvknGmff85tfbpwdq2qcPVwM/sSjG+X2jl7a40pmRr9+4m3GHR0ba5xte+WaNnXvHN7dcvRmRunf69XGfN8abRlslhtI/07Xu214V+NstfiJLxzoz6/3gwPNX+vhgYY/4I7RsdHG2SULFjbO7m0xbptt0Q923N44+4BjTmucBfjBfbe2ys91zx87vlHuywNre1zJ5I20eJ9Wi9+F23zPD7T4xt05srlxtp/a/D431GK7/9Vsa5wdHh1pnAVYtXxl4+wd22amZzKvG2ZVtZ7+/v4rSZIkSZKkWcarZEqSJEmSJEldbJhJkiRJkiRJXeZlwyzJXyT5837XIUmSJEmSpNlnXp7DrKpe2O8aJEmSJEmSNDvNyz3MJEmSJEmSpAM5LBtmSZYmeWuSm5Pcl+SfkpzdNf+KJJd3Pa4kv53ka0m2JflKkvt3zV+Q5OVJ1iXZnORLSR4+YX3vS/Kuzvw7krxgijWvTLI6yeq9Y3vbvgSSJEmSJElq6LBsmAHvAu4P/CxwIvBV4ONJhg6SOR94CnAscBtwWde81wJPBn4JWAn8NfBPSY7uWubXgH8EjgEuBP48yelTqPlCYC2w9ofbb5tCTJIkSZIkSb102DXMkhwLPAP47aq6u6qGGW94nQQ84iDRt1TVrVW1B7gCeHhnfQFeDPxBVd1UVaNV9W7gTuCJXfnPVtU/VNVYVX0E2Aw8ZAqlXwasAdacvOzUKcQkSZIkSZLUS4fjSf/P7Pz7nfFe138YAg7Wibqz6/4OYHnn/rHAMuAfk9SE9Z1ygPzEdRxSVW0ENgL8/KpzJxuTJEmSJElSjx2ODbNbOv/er6o29GB99zLe/PqFqvpaD9YnSZIkSZKkWeywOySzqu4BPgC8M8kqgCRHJfmVJMsarK+AtwNvTXK/zvqWJXl8kpN7WbskSZIkSZL677BrmHVcwPgJ9K9Nsg34LvBUoA6aOrDXAB8DPpZkK/DvwAs5fF8/SZIkSZKkeetwPCSTqtoJvLJz29/88yc8zoTH19L12lTVCPAnndsh19eZdsaUipYkSZIkSdKsMGcaZp3DK6+Z5OLnVtUd01nPdHpBndQ4+2Wu72ElOpgFA4ONsyNjoz2sZGpy6EUOqOkumm1d/wffbJwdbPFzWjI4ZzaR02fhkn5XoGnUr890v8duqk3NA2mz9QVqLr5ivfMTu0ca5QbT7mCAvX36vh5oUffu0b2Ns0MtvjPHxpq/R9v+nCrNxx4ebfbeamvz8PbG2Z179zTO7m35fE9edkyr/Fz32NXN/sSsr7Tbhi9I889mm8/1rrHm77U2z3i0xhpnj1p0RIuR4b5d2xpn0/a7vqHNe3Y0zl5z3w8aZ4fH2m1Pdo8MN84OzdDfaXPpr8EhYM0UlpUkSZIkSZKmbM40zKpqPe12jpEkSZIkSZIOyZPWS5IkSZIkSV162jBLsj7JM3u5zkOMd16S62ZqPEmSJEmSJB3+5sweZkmuSHJ597SquqqqHjzDdZyRpJKcMpPjSpIkSZIkaWbMmYaZJEmSJEmSNBOmo2F2VpIvJtme5OtJfrp7ZpILknwvyZYk30ryuK55D+1ktyS5L8m/Jjk6yUuB84Bndda7PclgkvOT3NCVvzbJnyT5+yTbktyY5Nwkv9AZc2tn3vKuTCV5UafWHZ0xT0nye0luS7IxyRu6nsK+Q0DXdup4VWc9K5O8u5PZkOTqJCdM9kXr5FcnWb2txeV7JUmSJEmS1M50NMxeCFwEHAP8LfDJJCtgvFkGvIzx5tfRwCuAjyQ5u5N9B/DpTvYE4CXAcFVdAlwFXFlVyzq30QOM/xvAm4CjgA8B7wOeDzwaOANYA7x4QuaZwP8EjgN2A5/t1PdjwGOB/53kv3WW3XcI6JpOHa9PEuCjQAE/AZwObAM+MMnXDOBCYC2w9jM71k4hJkmSJEmSpF6ajobZu6vqG1U1DLwZ2AU8qTPvIuB1VXVdVY1V1SeBzwFP78wfBk4DTq2qvVX1laraMcXxr66qr3Yaau8HTgLeUlX3VdV9wMeBh0/IXFpVt1fVTsabfCcCF1fVcFVdx/heZRMz3X6qc/udqtrSWc9LgcdO4VxnlzHezFvzi0esmWREkiRJkiRJvTYdDbP1++5UVQG3AvuaRmcC70iyed8NeAywqjP/2Z2avpjk5iSvT7JgiuPf2XV/5wGmLedHTZx/T1WNHSLT7UxgEXB31/O6kfG91U6bTNFVtbGq1lXVuuUDiyYTkSRJkiRJ0jSYajNqMs7Yd6dzqOJpwO2dSbcAr6mqD+8vWFU3A8/pZB/E+OGZNwN/DYztL9MH+6vjFmAHcMyERpskSZIkSZLmmOnYw+w5SR6WZAj4A2Ap8InOvLcBFyd5SMYtSfKoJPcHSPKsJCd3lt0MjAD7zlV2F+MXFOj3lT03MN40u1/XtK8zftjmnyVZCZDkuCRP309ekiRJkiRJs9h0NJ/+CvgzYBPwNOCJVbUFoKreBVwCvKcz/1bgVcBQJ/tY4BtJdgBfZvyk+e/rzLscOALY2DnscXAaaj+kqtrFeM0f7NTxis5eZU8Gwnj924CvAOf0o0ZJkiRJkiQ119NDMqvqjM7d1x5kmSuBKw8w71kHyd0EPGLC5Cs6t33LnDMhs57xJlb3tIsnPJ44/0fWeYD1vhF444Rp9wG/07lJkiRJkiRpjjpkwyzJKuCaSa7v3Kq6o11Jet59/9LvEjQJY3P0dHXV7wIauP97n9w4u+LJaxtntw7vapw9XOx6xUX9LkHTKIde5KDm4vakzXNu83yHBtv9H+Wzjv+ZVvm5rhr+5EZbflcPpPk7Ji3ebW3r7ofRsblXM8DCFp/NkbHRQy90AGMttihtxm373mpT9+Fg7fXHNUze2NM6pmKsmv/M0mIbWC3GbVPz9r27G2cBBgb6cwaoFYuWNs6O7t7eOHvs4iMbZ+/etalxFtptj4YGZuaAw8l8QwwBaya5vqFDLyJJkiRJkiTNXodsmO3vsEZJkiRJkiTpcNXvK05KkiRJkiRJs4oNM0mSJEmSJKmLDTNJkiRJkiSpiw0zSZIkSZIkqYsNsx5Jsj7Jy5Nck2R7ku8leeQU8iuTrE6yuqr5paIlSZIkSZLUjg2z3noO8GLgSOAzwJVTyF4IrAXWjo5un4bSJEmSJEmSNBk2zHrrL6vq+zW+i9jlwNlJjpxk9jJgDbBmcHDZtBUoSZIkSZKkg1vQ7wIOM3d23d/R+Xc5sOVQwaraCGwEWLz4tN5XJkmSJEmSpElxDzNJkiRJkiSpiw0zSZIkSZIkqYsNM0mSJEmSJKmL5zDrkao6Y8Lj9UD6UowkSZIkSZIas2E2CxXV7xI0CWPlz2mmnP0rlzbOrv/wixtnVzzpDY2zh4un/rNfEzOhzf+utNkStd2K9avufmnzfB941Omtxl7GYKv8XHfV4t3NglvbjTuY5gdjLBxsvv3cNTLcOJsW79S9Y6ONswNp9//EIy3Grha/kw2PjjTODg00/1wOpXl26dCixtntww0/Sx3DNH+9DgefX7SwUa7NexTa/X24cKD5tmhPi21Rm23C4gVDjbPbhnc1zgIsaPG5bvP3YZtt0WiNNc62eW/tGdnbOAswsKD5e6TNd+xUeEimJEmSpL5p0yyTJGm62DCTJEmSJEmSutgwkyRJkiRJkrrM6YZZkoVJPpRkU5J7O9POS3Jd5/73kzytv1VKkiRJkiRpLunZmdKSXAv8HDDxzG8/V1Xf7dU4E/wa8DPAqqraCVBVVwFXde4/cJrGlSRJkiRJ0mGq13uYvb6qlk24TVezDOAs4MZ9zbLZKEmzS6lIkiRJkiSpL2bskMwk1yb5kyR/n2RbkhuTnJvkF5J8L8nWzrzlXZlK8rtJvt3JfC7J2Z15fw68GjgnyfYkV3SmvzfJ7Z3lf5Dk17vWd06SkSRP64y/JcnVE8ZcneTznXquS3JRkuqavyDJy5OsS7I5yZeSPLxr/hVJrur8ex/w9km+Pis7Y6+u8kpBkiRJkiRJ/TLT5zD7DeBNwFHAh4D3Ac8HHg2cAawBXjwh83zGD708Hvg+8A9JBqvqRcAbgWs7e7Kd31n+88CDOmO8HrgyyQO61jcIPA54MLAaeOi+MZMsAP4RuA44AfgV4IIJ9bwWeDLwS8BK4K+Bf0pydNcyTwU+BRwH/P4kX5sLgbXA2tHR7ZOMSJIkSZIkqdd63TB7RWevq/+4TZh/dVV9tcZ3oXo/cBLwlqq6r6ruAz4OPHxC5tKquqGqdgEvBX4MeMSBCqiqd1fVpqoaraoPAt8Bzpmw2B9W1faquhv4aNeYP8t44+5lVbWrqm4C3rYvlCSMN9f+oKpu6ozxbuBO4Ild6/9iVX2oM3+yh4texnjDcM3g4LJJRiRJkiRJktRrPTvpf8cbquqPDjL/zq77Ow8wbTk/av2+O1W1M8kG4JT9rTzJAHAx8DTgRKCAIxjf02uf0ara0PV4R9eYq4B7Os25fW7pun8ssAz4x+7DNIGhCTWtZ4qqaiOwEWDR4lOnGpckSZIkSVKP9LphNh3O2HcnyVLGm1+3H2DZXweex/ghlz+oqrEkXwcyybHuAI5LsqSraXZa1/x7GW+w/UJVfe0g6xmb5HiSJEmSJEmaZWb6HGZN/F6SH0uymPHzn90EfPUAy64ARoANwECS5zB+rrLJ+gpwK/DHSRYnORP43X0zq6oYP4n/W5PcDyDJsiSPT3LyVJ+YJEmSJEmSZp9eN8xe1bliZfftSS3XeTnwEcabYA8GnlwHvozklYw3025gfG+xBwBfmOxAVTUC/DLwsM54H2X8wgTDXYu9BvgY8LEkW4F/B17I3Gg+SpIkSZIk6RB6dkhmVZ0zlflVtZ4Jh0pW1cX7iX69qv70AOu8eMLjnYxfofJANVzLhOe8n3Vcz/hVOwFI8gK6zmPWaar9See2vzHOP9D4kiRJkiRJmv0O2jBLsgq4ZpLrOreq7mhfUn8leRTjFyK4CXgQ41fmfP9M1rDpjx7fOLvi5Z/qYSUz59TlxzbO3rbt3h5WotloeGykcfbEX720cbYOvchh72Pv/bVGuWOfeH3jMfeM7G2cbWuyJ7zsteWLljbObt0z2Ysx995c/Iz0q+Zvb7yxVf5bdUPj7CWtRp4dBvv06Rwebf79s2TBwsbZRYNDjbO7RoYPvdAss2BgkL0tXut+WdziZ3zsoiMbZ3+4a2Pj7JGLlrJ5z47G+X5+R88Gb9rwpUa5ocEFDLd47dp8Psaq+TffgsHm+9eMtKh5+dCSVtnRan5K8Q27tjbOtvk5tflcVouf8a7RPY2zxy5dwZ7R5u/rNtvBNj/jqTjUJ2AIWDPJdTX/Zp9dTgU+wPgVMTcAHwb+uK8VSZIkSYepudgsm6va/FGu5to0yzQ1M9VIEa2aZXPFQRtm+ztsciZV1YyPXVUfBD440+NKkiRJkiRpdvBE9ZIkSZIkSVIXG2aTkOTyJFf0uw5JkiRJkiRNPxtm0yDJ+iTP7HcdkiRJkiRJmjobZpIkSZIkSVKXvjTMkpyY5B+TbEmyLslzk1SSM7qWuSDJ9zrLfCvJ47rmPTTJFzvz7kvyr0mO7sxbkOTlnfVuTvKlJA/vyv7/9u48XpKqvvv45zsbIAww7PuaGRRQjIgoKpHRIT7GDYzRAApBeTRRUEmMjwoiSFyIiRCNAgKyJLglGFYREJCoEQLGALLLMIwsIrOyzvp7/qi6ULdv9+3qru6uqu7v+/Xq1+2urlPn9PbtuqdP1TlX0gWSvpne/5CkDzS070hJv5G0XNIFwLoN9+8g6d8kPSrpEUlnSpqZ3ncpsANwlqQnJV3Vh6fQzMzMzMzMzMz6pKwRZv8KrAS2B14DvCd7p6SjgE8AhwKzgE8DF0n6g3SVfwauAjYBtgSOTbcHcCLwNuCNwKbAOcCVYx1qqT8FLk3LHw18TdKOad2vTbf/wfT+q4F3Zdq2LnAtcAewM7A7sB1wGkBEvAV4EHh/RGwQEc919E1G0qaS5kias+ipZ/MUMTMzMzMzMzOzPhh4h5mk7YC5wMcjYnlEPAZ8rmG1jwAnRcT/RsTaiLgCuA54d3r/SpJRXNtHxKqI+EVEPCVJwDHptu+PiDURcTbwCPAnme1fGxGXpNu+CFgKvDS9773Av0XE1RGxOiLOB27KlH0zoIj4TEQ8ExFLgOOBQyVNLfDUHA3cDdx9+s/uKLAZMzMzMzMzMzMroowRZtumfx/MLFvQsM7OwD+nh0wulbQUOCBT9i9I2v5TSfMlfU7SNGAzYAPg0oayu5CMAhvzSEN9TwEz0+vbAQ803D+/oW07NGz/x0AAW7V78JP4KrAbsNsHX717gc2YmZmZmZmZmVkR00qo86H07w7A/ZnrWQuAEyLi+802EBHzgSMBJL2Y5PDM+cC3SDq/3hAR/12gfTs1LNsJuC/TtnsiYo9JtrG200ojYhGwCODpL7+/0+JmZmZmZmZmZtYjAx9hFhG/Ba4HvihppqTNgeMaVvsK8FlJL1ViPUmvkfRCAEmHS9omXXcpsBpYExFBci6xL0uana67gaQ/zqzfzgXAn0p6fTqBwGHAvpn7LwNmpBMLzEzbt62kgzLrPArMzvucmJmZmZmZmZlZdZR10v9DgBcAvwV+BoyNJFsBEBHfBE4hGTG2hOTwzeOB6el6c4FbJD0F/BdwIUlHF8AJwMXAxZKWA/eSnMA/12ONiJ+QnE/sLGAxyeQB383c/3Ra/+7AXcAykkMyX5rZzMnAYZKWSPphnnrNzMzMzMzMzKwayjgkk4h4hOTk+QBI+mOSzrJHM+ucB5zXovzhk2x7NfCP6aXZ/Uc0WbZTw+2zSDrMWtWxEDhskvuvAK5odb+ZmZmZmZmZmVVXzzrMJG1LMtIqj/cBTwC3kZxE/2Tgu+khlWZmZmZmZmZmZqXp5Qiz6SSzPOYxB/g0sDXJIY0/BP66h22ptTUPPFx2EwZu83U27rrswice72FLrIqeXPls12VXr1ndw5aMntsPu7yrcitWr+pxSwajrF9ttlxvVtdll694uoctGRwVKFvkdZo6pfuzUaxd2/GcPs+ZPrXYLteaAnUPgwdWL+uqXNHfYqPAu+2pVSu6Lrv5eht2XfbpAvXOKPA+XTuCv3tPnzK167Ir1nb/PSl1n6BTVeyMPFOnlnVGn2pY1eV+5ZQC3z1Q8Dtg7Zqui86YOr39Si0U2QNftrL7fZunVnX/fwOACuyhFNm3KcuUAq0uum+yfNVTXZfda+aOherOq2cdZhHxAJ29R77Vq7rNzMzMzMzMzMx6ZbR/IjAzMzMzMzMzM2vgDrMWJK2W9Lqy22FmZmZmZmZmZoPlDjMzMzMzMzMzM7OMke4wkzRNKnjmSzMzMzMzMzMzGyqV7yySdLCkezK3T5IUknZJb79C0jJJ09Lbe0r6kaTfS3pQ0hckTU/v2ykt+z5JdwBPAVtIminpPEmLJS2QdHiTdrxd0i2Slkq6U9KhmfuOkHSfpGMk/VbSEklnSOp+6hwzMzMzMzMzMytF5TvMgGuBXSTtkN6eB9wHvCFz+ycRsVrSFsBPgIuAbYFXpfd/smGbhwBzgZnA74FTgdnA7sBLgLcBz3V2SZoHnA18FNgEOBz4mqT9M9vcEdgS2BXYB3gn8O68D1LSppLmSJqz6NmVeYuZmZmZmZmZmVmPVb7DLCKWAr8E3iBpQ2AP4O9IOsIg6Ti7Jr3+XuB/I+KMiFgZEQ8BX0iXZ50YEY9GxEoggEOB49Nly4BPNKz/EeC0iPjPiFgbETcB/9Kw3WeAz0TEioi4D/gx8PIOHurRwN3A3WfcuqCDYmZmZmZmZmZm1kvTym5ATteQdIwtAv4LuAL4sqQNSEaRfShdb2fg1ZKWZsqKzGix1AOZ65sD6zQsm9+w/s7AAZKOzSybCvxn5vZjEbEmc/spkhFseX0VuBDgAy/Z8e4OypmZmZmZmZmZWQ9VfoRZ6hqSQyjnAVdHxGPAQySHSC6KiDvS9RYA10TExpnLRhGxQcP21mauPw6sBHbKLMteH9vuZxu2OzMi3tSTRwdExKKIuCci7tl03Rm92qyZmZmZmZmZmXWoLh1mPwM2BN4DXJ0u+zHw8fTvmPOBl0s6UtK6kqZI2kXSG1ttOB0VdiFwoqQt08M+v9iw2qnAxyS9VtJUSTMk7S2pk0MuzczMzMzMzMysBmrRYRYRK4CfAs8Ct6aLryHpRLsms96jwAHA20kOsVwC/ADYpU0VHyE5DPMu4DbgUuC5wysj4irgKODvSUakPQJ8BWgcuWZmZmZmZmZmZjVXl3OYEREHNty+guT8ZI3r3QG8tcU2HmhRZjnJ6LWs8xrWuRy4vMV2zwXObVh2RLN1zczMzMzMzMys2krrMJO0LeMPp5zM69MZL83MzMzMzMzMzPqqzBFm04HdOlh3ZKxz7Ge7L3zmQT1rxyD98vH7ym6CVdiqNavLbsLIevEVR3VXcJ+betuQGpgwfLkD9y9/pGftqIsoqd61a9e2X6mFIm1esXpVgdL2u5XLuio3Y2qxXd010f37ZeqU7s98Mn1K97u+UeCdus36m3ZddsETj3VdViqSoDBF3T/XUwrUXeT98btnlnRd9tk13efJtClTuy4LsM7Ukfq3bIJ/3OJ1XZX7xKKfFqpXBfYy1pvW/YRyK9d2vw8+pUAGPr1qRddl1xT4nodin5EiWVYkiyhQtshr/MzqlV2XLeqWZfcPpJ7SOsxaHR5pZmZmZmZmZmZWplqc9N/MzMzMzMzMzGxQ3GFmZmZmZmZmZmaW4Q4zMzMzMzMzMzOzDHeYmZmZmZmZmZmZZbjDzMzMzMzMzMzMLMMdZhUhaVNJcyTNWbSku6nTzczMzMzMzMysOHeYVcfRwN3A3V8//3tlt8XMzMzMzMzMbGS5w6w6vgrsBuz2V+/9s7LbYmZmZmZmZmY2sqaV3QBLRMQiYBHAyvtvKrk1ZmZmZmZmZmajyyPMzMzMzMzMzMzMMtxhZmZmZmZmZmZmluEOsz6RdLqkH5bdDjMzMzMzMzMz64zPYdYnEfHBsttgZmZmZmZmZmZdiAhfanQBNgU+C2w6CmXr2u46lq1ru+tYdhgudX3e69juOpata7vrWLYX5et8qevz7rL1qHvUypZdd50vdX3eRy4am5AAACAASURBVK1sXdtdx7J1bve4bRXdgC+DvQBzgADmjELZura7jmXr2u46lh2GS12f9zq2u45l69ruOpbtRfk6X+r6vLtsPeoetbJl113nS12f91ErW9d217FsndudvfgcZmZmZmZmZmZmZhnuMDMzMzMzMzMzM8twh1n9LAJOTP+OQtky6x61smXWPWplh0Fdn/c6truOZcuse9TK9qJ8ndX1eXfZetQ9amXLrrvO6vq8j1rZMusetbJl1t2zLFJ6jKeZmZmZmZmZmZnhEWZmZmZmZmZmZmbjuMPMzMzMzMzMzMwswx1mZmZmZmZmZmZmGe4wMzMzMzMzMzMzy3CHmZmZmZmZmZmZWYY7zMzMzMzMzMzMzDLcYWZmZmZmZmZmZpbhDjMzMzMzMzMzM7MMd5jZUJGkTpY3rPOeFssPLdquKpI0XdKfSVqn7LbUQfp8XS5p3bLbYtXnLMrPWdQZZ5F1okgWpeuNTB45izrjLLJOed8oH2dRZ/qdRYqIfmzXekjSLsC7gW0j4kOSdgOmRcSvB1D3RsCfANtFxCmStgKmRMTDOcvPBGZml+Up2+1jlrQ8IjZssnxxRGzSr7J11eoxtykzHfg08IfATcCXImJ1F3VvQPLe2h5YCFweEU92up2cdZ2TZ72IOLLNdh4l+Sx0/HiHRVl55CxyFjUp4yxyFjmL+phFvShfN2VmUbqtgeSRs6h3nEX5H6/3jfLrJovScrXaN6pDFk3r9QattyTNAy4CrgNeB3wI2Aw4Dvg/OcrPB5r1iq4AFgAXRsT5LcruDVwJPALsDJwCvAT4APCONvW+CjgP2DW7OG3L1DZlizzmCb9Q5P0VtUXZnYC+7wRIWh84Bng5E7+8Dmyy/iqav67jRMSMNqvcLOklEXFrB809BTgEuAH4CLA+8KkOyiNpD+BqYA3wALAT8BVJB0bE7TnKC3g/8HpgczKvXUTMbVJkTeb6usC7SL5E5qd17wt8J0fTLwA+DJyaY92hU+Sz6SxyFrVZxVnkLMrNWTSwLGpVfif6nEejlkVQLI+cReVwFnX2fyk13DeqWRZByftGw5hF7jCrvi8C74yIKyUtSZf9EnhZzvLnkITnOSThuyNwBMmbajrwj5K2jIi/b1L2VOBvI+Jbmbp/DnwrR71nAJcBZwFP5WzrmI4fs6Qz06szMtfH7ALcPUnZsWCbKmllw91TgW/kbbike4EzgfMi4rG85Uhenz8E/oN8z9cbOtj2ZK4DLk2fswXA2rE7IuLCFmUOAuZFxK2S/pAkxDrdMTyV5D1yUkREGq7HAaeRBGw7fwccSfI+fgvwdeA9QNM2R8RRY9clnQ+8LyIuyCw7DJjwpdfEy4CPSPoQE5+vPOXrrkgeOYucRZNxFuEs6oCzqI9ZlJbvSR45izpSJI+cReVwFuV4vFXYNxqRLILy942GLot8SGbFSVoaERun158bcqr8Q+lvAI6JiF9llr0U+KeI2F/S/sBZETGnSdnFwKbpByVb93NtmqTeJ4ANo4s3WDePWdLYF8ShwL9m7loLPEryGOe3KPtHJL3fVzD+15G1wKMRcW8HbT8SeB+wN8mX0ZkRcVWOckuAORHx+7x19UL661YzERG7tCgzboiwpEURsWmH9S4CtoqIVZll00me77bbkvQA8LaI+F9JSyJilqRXkuw8HNym7FJgk4hYm1k2FViU4319Qqv7IuLEdu2uuyJ55CxyFrWp11mEsygvZ1F/sygt35M8chZ1VHfXeeQsKoezKN/jrcK+0ShkUVqu1H2jocyiiPClwhfgNmDP9Pri9O9ewC9zll8GTG1YNg1Ynl4X8GSLsncDOzbU/QfAr3PUew3wwkE/ZuDjBZ7rrXv4ur0I+AfgdyRDSo8jOda/1fr3Aet1sP19M9f3a3Xp03tyecPtxV1s4zfA7IZls4H5nbYBeHzsPQ4syVH2PmBuw7IDgPv78XwN06XgZ9NZlL9eZ1G+up1FI3pxFg0mi9LyPckjZ1Gu7XSdR86ici7Ooo7/Ly1932iYsyits9R9o2HMotIq9iXnCwRHAfcAh6XB+o40rN6Ts/zNwLENyz4G3JJe3wp4pEXZTwH/BbwGWELSI3898NEc9X4ybfexJMdRP3fp92Mu+Hy/hmS47qXp7b2B/QtsbzZwC8mvICuB7wLbN1nvz4BvkvSq59nuE5nra1tc1nTQzi1Jjs3fIse6a9LXZ+yyuuH2PTm28Zl03SOBuenfu4ATcrb3TmCH9PpNwJuBVwKP5Sh7JPA0yfkbTkz/PgUcmbPuXdLPxtfS23OAPfr93qzCpchn01nU8XPtLGq/rrPIWeQsGszz3bM8chZNup2u88hZVM7FWeQsarJ+aVmUrl/qvtEwZlHf39i+FL+k4XQb8CRwO/D+DsruS9K7Ox/4Sfr3cdLeb2Ae8LEWZaeSHIe8PP1wLwc+RzIDS7t657e45Ooh7vYxpx+MHwGL0vB77pKj7CHAYuCfgGXpspcB13f4ek1Pw/Wq9EP+bZITY+4EnA78Kl1vVUMb16Yh11G7C763ZpEMS16bqf9SJvlSAA5vd8lR71SSL+y702C8O709LWe7jwEOSq//OcmXwRrguHTZ1qRh3aL8a0m+cK8g+RLM9WWbfl6eAC7h+V8AXw38sJ+vU5UuBT6bziJn0WRtdRY5izp9zziL8pXrOovS8oXzCGfR4Tnr7jqPcBaVdinw2RypLErLlrpvxJBnUVqu1H0jhjCLfA6zEaBk2uG3ANsADwGXRcSyDrexWUQ83o/29ZKknwO/Bc6l4cSMEfGTNmV/TRIiN2eOuZ4BPBQRm+es/1SSX10WkXzIz80+b5KmAUsjYoP0mPy22rW7CEnnksxucyzJ8NtdgS+TDN89okWZP4+Ib/erTd2QtB2wQUTcld6+k2QocU8nNpF0C/DpSE92mr5H1gMeiIgte1nXMHIWJZxFEzmLOq7HWVSAsyiR5zNdNI+cReVwFtXDKGURlLtvNApZlJarVB4NQxa5w6wmJM1k4lS2D5fUnL6TtAHJEM7tgIXAFRHxRI5yy0lOgrmq3bpNyi6JiFnp9cURsYmkKcDjkWOChbTcd4DTI+L6SdbZOyJuaVi2f0Tc0GTd10bEf+aodx7Np+89sk25h4EXZb+cJc0C7oiIrVuUGXcyyW5IujMiXtRk+W0R8eIi2x7bPskJOptOjy3pNcB7Sc6H8BYl03Ov3+w1aChXaBKOYTFKeeQsem65s6jL7eMs6htnUX+zKC1fKI+cRfn1M4+cRf3lLGqfRWnZ0vaNRiGL0nUqvW9Uxyzqac+e9Z6kV5Ecv7trdjHp9Lo5youkZ/r9wPYkwXYW8JXIzECRWf/edNuTiiYztjRsZz2Skyg2C4iWM3ukZV9OMgzzGeBBYAfgq5LeFBE3t2naXcAWJL/SdOo3kvaLiJ9nlu1H+6nXP5W5eSuwn6T9GteLiM+nf29pvI9kyG2zcLsYaDfLzkeALwCXk3yBXUYyi8xFk5UbK87E13stmderRZmitutwec9IOgT4GvAvwP7p4gBOIhmWPZmFkvaMiNsz29sLeKD3La2eInnkLOqIsyjhLGrNWeQsGkQWQRd55CzqWil55CzqnrOooyyCAe8bjWAWkeP+PJxFGe4wq74zSD5cZ9EwdDWnT5GcQO9LPD+c82+B9YCTm6zfbFk3vkJyYsZvpHV/Avgw46cSbuXrwD9ExJfGFkj623Rb+7Qp+y3g3yWdQjJN8XMaAraZk4GLJZ0GTJf01yQn3zyqTbl5be6H5MP++UnunxBu6a9VE74wm/gw8KaIuD799eWdkv4EmHTq3tTVwAWSPkYSKDuRzBxz1SRluh6WmvnimtbwJQbJ7D4Lu912Bz4NHBjJkO73pMtuB/bIUfafgIsknQRMlfQO4LPAKX1pafUUySNnUcpZ1JSzKOEsysdZNJgsgu7yyFnUgQrkkbOoe86i/FkEg983GrUsgnrvG1Uzi6KPJ6vzpfiF5OR1KlD+PhqmDgZ2o8/Ts5L8crBLen1p+nd34Mc5H/O0hmXTyMw6MknZQrORkATrpSQfzsuBeX1+nu6lxQwmwFLg+3mer8z1sSmeRTJEuV3ZTYAref5kkmuAH5IMl4YmJ2ZsWLfpZZL6rksvqzPXrwN+TPJF/fJW9Xb4vN7Zqh1kpjXOPF9TyDntMgVOdlr3S5E8chY5i9qUdRaFs6iD59VZNKAsSrcxsDwatSxKy/c9j5xFfXu/OotyZlG6bm32jeqYRZnnuLL7RnXMIo8wq74bScLzri7Lb0Lyq0XW/cDGzVaWtFVEPJpe36bVRqP9cfkbRMT96fWVkmZExB2S8vz68Ctgz/TvmBc33G7Vrik5tj+BpOnAQcDFEXF1N9vo0skkwfkNktluxqwl+eXluhzbeEzSlhHxO+C3ksZm3Wn7XETEYuCNkrYmHQ4eEY9kVrmWZNrlbFY8AxyYo13N6jsAQNJXI+LoSVZtVm+vdHW425iI+CbJyUJHUZE8chbl4CxyFuEsysNZ1OcsgtLyaKSyKK2z7DxyFnXPWZQzi9K21WnfqI5ZBPXeN6pkFrnDrILS43fH/Bi4RNLpTBy6emGOzf0P8HHGDzX9G1oH2708f9LK3zJxWGfe86fNl/SiiLiT5EvkSElLgaYzvzQ85quAyySdBSwgGYJ6JMkUs7mk5wXYqiFYWoqIVZLOiojv5a2jFyLiPABJd0XEL7rczHdIzkNwIcmQ8LFfBs7voB2PAK2eq8ahyGsi4mddtDNb32Qh3KreXun2cDcknQ2cHfkOYxkKPcwjZ1EOziJnEc6ippxFg80iKCePRjWL0jrLyiNnUQecRcWyKN1m5feNappFUO99o0pmkWfJrCBJ83OsFtHmxIzptl5CEm4rSIJtR2AdkuODb22y/vYRsTC9vuMklS9oU++7SIb5/kjJ7CA/AGYAfxkRZzdZP/djTnvap0fEg022swFwGnAoSWCsL+ntwF4RcWKbNl8LfLTZ89IPPfylqHG7ryb5Qv1RFPyAq8lMJpKeiIiZkxQrrFm9vSyfviePAXYm+VycmucXKyVTPL+DZDj7t4DzO/nno456lUfOImdRwTY6i8aXOxdnUTPOoonb6TqL0vIDyyNnUed196qssyg/Z1GraifPonRbtdg3qmsWpcsrvW9Uxyxyh9kIkLQh46f/vTwilrdY978jYp/0+gl5dqZytmE6MCMiupm4oHFbdwKzI2LCCElJZwDbAicA10TELEnbAldHxO5ttns8yUw1Z5J8QJ87mWOOX4k6psy0v5LW0uKXom7/UeuFFjuG34iIvxx0vYMs32bb6wPvAo4AXkmys3N2RPyg13UNG2eRs6hbzqKm23YWdclZlC+L0vIDyyNnUed1D6Jsjm07i7o0SlmU3l+LfaO6ZlG6vNL7RnXMIneYVVT2gzrgepcCsyIiirQh7UF/JJ4/Rh5Ju5AMvy00VHKyD5qkh4DdI2KZpMURsUm6fGlEND0nQKZsq19Q2v5K1I0e/lK0PklP/Mt5fqj2WNmuz6mRbrvZjuGZEfF/M7dfERE3FaknT70dlr+L5At7anq7X78U7UoyK8sby/zS7Lcy8shZ1JSzyFnUqh5nUf/qHNksStcbWB45izqvu4OyzqIecha13Ha70UO12Deqaxalyyu9b1THLPI5zKqr6+OCNXEa2KYiotkUujcCN6QfhHUlNT0mPftBbOEM4K2NTUuXvzhP+7o0heRkh89Xmgz/fbJdwYjYuV+NalFfdmrehRGRZ3riZs4HXkgyrfXThRvW3ruB7Ot/JcmJS6tkLjA9c7tX531IVpY2Aw4j+QVjNvDtAm2tg67yyFnkLOozZ5GzKF8hZ1FXWQSDzSNnUV85i3rLWdSdWuwb1TiLoPp5VLsscodZdRUZ+jcv5/Y/r4nHmr8b+CAw1lM/vVlhgCZls3bI/nIBEBG/mayXvkd+CnwSyA5TPpocM5lImk0y/e+jmWVbkcwmc1+vG9pgsaTrgWtIhih3MtvO64GdImJpX1o2UeNOQr9OiN21Jr9CZId5d/2FK+mtJAH8JpKTtX4d+E60GD4/RLrNI2eRs6ifnEXOorycRV1kEZSaR86iHnIW9ZyzqDt13DeqUxZBxfOojlnkDrPqWlfSOZOtEBFHtlh+QAf1jJsWNiKWAF8AUDIN7l/kLdvg95J2yAZ1GsSLO2hbN44FrpV0GLCBpNtITmQ5N0fZb5N80LKz3GwOnA28osftbHQg8AbgYODvJT1OMvPO1RHxr23KLqRYB2unGuuqw3HdFwFj02UfEd2f9+F04ALgpR1+YdZdV3nkLHIW9ZmzyFk0gbNogiJZBOXlkbOov5xFxTiLulPHfaM6ZRFN6qt6HlU+i3wOs4qStBI4b7J1IqLtFKs56unLSfskfZnkzf8BkqGWs0l6en8VEcf2s82S1iE5gebY7BqXRcQzzdZtKDfh+HlJApa0O66+l9Lj3T9KMtX0zHavjaTXk0y3ewoTp7Tu6HjvJttudq6OZ4GTMqsdRzINcLbeZkPJC9VbcHu9Ou/D1IhY04s21ckg8shZNK6cs2jitp1F47fjLGrBWdT0/q6yKC1beh45i1rXXWBbzqICnEXdt7nO+0ZVz6J0ea32jeqQRR5hVl3P9qJDrEQnAOcAd/B8z/a/AcdD26HChUTECuDf03rWJTOLShvLJG0WEY9nlm0GFJ41ph1JO5H8ejEPOAB4mOQXk2tyFA/gtcA7s5ukg+O9J2tak2W/YPyQ8hsbbrcaSl603iK6Pu+DpH0j4sb05r7Jd3PTsoVOlFpxdc4jZ1EHnEW56i3CWVSMs6gLBbIISsojZ1HuurvlLCrGWdSluu0b1SyLoH77RpXPIo8wq6i8PaxFQ61fv15k1tkc2Al4ICJ+31C25bTD3dYr6WTgkoi4SdI84GKSID44Iq5qs92zgI2Av4iIJ5WchPIs4Jk2w54LUzJl8b3A3wE/zD5XOcreC3wP+BcaTigZbWZvybHtbUjeXx1vp+Br3HW9LbY3i+fP+3A40HQIdbPXWdITETEzvd7qSz16NQKligaRR86icWWdRRO37SxyFjmLuqi3SBal5UvJI2dRb+tusi1nUQHOou7qreO+0TBmUVq+EvtGdcgid5hVVPYN0Ga9ol++fQ3jQZeVtBDYIyKWS7oB+D6wHPhwROzTuH5D2c2AS4GXAY8BWwC/BN7S8GtGz0k6ieTXiz2AnwNXkxwbf1uOssuBjaJiH+ZeDtftJUmXRETj7EA2iUHkURXzpEhZZ1F1OIuGh7Oo87JFsigtX0oeOYsGx1nUOWdRd2XruG80jFkE1cyjqmaRD8msqDwhnFGp2S8GYLLHu2EawusDewFzI2K1pFPbbTQiHpe0H8lx/TsCDwA3DyLkIuIzwGckzSQZ7juPZHjqMxGxTZvi1wB7Azf3uZlDoYpBXHXOo5acReM5izrgLOqcs6ilvmQRlJdHzqLBcRZ1zlnUUrvHWrt9I2fR4FQ1i9xhZnU0l9ZTKS+S9EJgT+DGNITXy7vhiAhJ/w0sjIhHetDW3CRtR/ILxhtIpiGeAdyUo+h84HJJ3wPGtTl6cJLZYSDp4oh4W3q95ZDviDiwzXamAf+PZMjwFhGxkaQ/BnaOiNN72WarBWfReM6iNpxF1id9yyIoL4+cRf3jLLI+mSyLoKb7Rs6i/qlDFrnDzGonJp9V5FTglvT6oenf/UlObDnpuQTSY+FPS8utAdaX9HZgr+h+ittcJN0N7Az8D8mvEYcAP4uIle3aTTI0+Q6SL589M8sDcBgnfpG5/rMC2xkblv0JkhOmAtwDfJFkOmMbIc6iCZxF7TmLrOf6lUXp/aXkkbOo75xF1nNtsghquG/kLOq7ymeRz2FWc0WPP67qMe5FSJoNrI6I+entOcCMiLh9snMJSDoD2JZk9phrImKWpG1JjlPfvdftbKj7IODaiFjW4v5C56orQxWPjS9K0gPAqyLiEUmLI2ITSQIWR8SskptXujrmibNoQt3OohpwFk2ujnlSxSxK1y0lj5xF9eAsmlwd86Tf79O67RsNYxbB8OVRP7OoVi+s9UWR4+oreUx+RNzbcPuehlVatfvNwO4RsUxSpGUfUjITSF9FxA9yrNby+ZY0FdgX2D4ivivpBclm45letXFYSDoYuCcibs8sezGwa0T8R5viLyA50WjWDODZ3rZyJDmLnucsGgHOospyFo1XSh45iwbHWVRZQ5dFUL99I2fR4FQ1i6YU3YDV3lySaVwHXbaKpgDjwisd/vtkOc3JR9KuwO3AFcDZ6eIDgW+W1qhq+xKwuGHZYuCUHGV/CTROa3wI+c5jYJNzFj3PWTQanEXV5Cwar3Z55CzqmLOompxF4zmLhl8ls8gdZiMuIh6OiAWDLltRPwU+2bDsaOC6EtrSia8C3wE2AValy64HXltWgypuy8ZzLETEQ8DWOcr+DfBFSdcAL5B0Kck5CD7R+2aOFmfROM6i0eAsqiBn0QR1zCNnUWecRRXkLJrAWTT8KplFPiTTylLFocLHAtdKOgzYQNJtJEM555bbrLZeAbw1ItZmhigvlbRxye2q4msM8LCkPSLi12MLJO0BPNquYHp+hRcB7wXuAhYA74+I3/WttdZvVXyfOot6q4qvMTiLbLyqvk/rmEfOos44iyyrqu9TZ1FvVfF1rmQWucOs/qr4Zs+j3bTDAxcRCyXtSXKM/M4kH7TLanCM+XJgY+DxsQXp8fxl76xU7jVOnQ98V9LfAPcCs0mG+p6Xp3BE/B74h/41r9bqmEeVe586i3qucq9xylnUP86iHqlpHjmLOuMs6h9nUY84i3quiq9zJbPIHWb1V8U3e1uNwy2rIiJWAP8OIGldYG25LcrlIuAcSX8FIGlTkmmbv1Nmo6r6GpME70bA94H1gaeAb5BMOzwpSecA50bEDZllfwQcFhFH9ae5tVK7PKrq+9RZ1DtVfY1xFvWTs6iHaphHzqLOOIv6x1nUQ86i3qno61zJLFJEFClvViuTTaEr6WTgkoi4SdI84GKSID44Iq4acFMb2zZZu9cDzgL+PF0UwIXAURHxrKStgekR8eDAGlwTkjZPf43Iu/5jJLPcrMgsWxdYEBFb9qONNpycRZblLLKyTPaZTu+vZB45i/rDWWRlquO+kbOoP6qURe4ws5HSJtQWAntExHJJN5D0bi8HPhwR+wy4qY1tm3SHNl1nU9IhytmAScvOjgiPKE11O8WzpEXAVhGxKrNsBvC7iJjV10bbUHEWGTiLrHw5OswqmUfOot5yFlkV1HHfyFnUW1XMIs+SaaNmsnMJbJiG8PrAXsA3IuI84A8G07RJtT0HQkQsioibW/TG1/EcCn2hYlM8/xp4d8OydwJ39KyBNiqcRSPOWWQV0e4zWdU8chb1iLPIKqSO+0bOoh6paha5N9NGzWTnElgk6YXAnsCNEbE6HUpbBbU7B0KFjU3x/DlgUbrseuC0HGWPB66U9GbgHpKTUb4VeFPvm2lDzllkziKrgnaf6armkbOod5xFVhV13DdyFvVOJbPIHWY2Utqc4PBU4Jb0+qHp3/1Je6bLPM68oidmrKuup3iOiJ9I2hf4APAy4AHglRFxaz8bbMPHWWQ4i6wCcnymK5lHzqKechZZJdRx38hZ1FOVzCKfw8wsQ9JsYHVEzE9vzwFmRMTtdT3OPM+x9aNE0v3AKyLicUmLI2ITJVM8Xx8Rc8punxk4i0aBs8jqYtjyyFk0nrPI6sJZNNyqmkU+h5lZRkTcOxbC6e17IuL2zCo+zrz+xqZ43g46n+JZ0msknSnp0vT23pL271trbSQ5i0aCs8hqwXk09JxFVgvOoqFXySxyh5mZjZrjgSeAB4GNgceAFcDn2xWUdAhwCfAsyTBwSKaIPqkvLTWzYeYsMrMqcBaZWRVUMot8SKZZTnUdNlvXdvdbqyme25T5NXB4RNwsaUlEzEqnLH4oIjbvZ3vNxtT1M13Xdvebs8jqrI6f6zq2eRCcRVZndfxc17HNg1C1LPIIMzMbVWMz2nTyJbVNRNycXh/7tWF1h9swM8tyFplZFTiLzKwKKpVF7jAzG34+nj9D0uaSfgQ8DNwEPCTpSklb5Cj+G0n7NSzbD7i71+00G0LOogxnkVlpnEUZziKz0jiLMqqaRe4wMxt+c4Fdym5EhZwJPAXMJvkFYzfgyXR5OycDF0s6Dpgu6a9JTkTpc3WYtecsGs9ZZFYOZ9F4ziKzcjiLxqtkFvkcZmY5+Tjz4SBpCbB9RDyZWbYhyXHys3KUnwccQ3psPXBqRFzdr/aaNXIWDQdnkQ0D51H9OYtsGDiL6q+qWTSt6AbMzGrmMWA9kl8sxqybLm9J0nTgIOBi7wiaWQ84i8ysCpxFZlYFlcwiH5Jplp+PMx8OpwDfl/Q6STtLOoBkyO6XJG0zdmksFBGrgLMiYsWgG2zWwFk0HJxFNgycR/XnLLJh4Cyqv0pmkQ/JNMsp/YBOj4gFZbfFuidpbeZmMP4Ldux2NBvSLela4KMRcWt/W2nWmrNoODiLbBg4j+rPWWTDwFlUf1XNIh+SaZZTRDxcdhusJ3YuUPY64FJJZ5IcG/9csEfEhUUbZpaHs2hoOIus9pxHQ8FZZLXnLBoKlcwijzAzs5EmaV1gbUSszLHu/BZ3RUTsImlrkl+3HuxpI81s6DmLzKwKnEVmVgVVySJ3mJnZSJF0MnBJRNyUzqZyMcmvEAdHxFUFt30nMDsiPHrXzCblLDKzKnAWmVkVVDWL3GFmZiNF0kJgj4hYLukG4PvAcuDDEbFPwW17Smszy8VZZGZV4Cwysyqoaha5w8zMRoqkZRGxkaT1gYeBTSNitaQlETGr4La9Y2hmuTiLzKwKnEVmVgVVzSIPjzWzUbNI0guBPYEb0yBer+xGmdnIcRaZWRU4i8ysCiqZRe4wM7NRcypwS3r90PTv/sAdAD5BrZkNiLPIzKrAWWRmhWgwegAAAVlJREFUVVDJLPIhmWY2ciTNBlZHxPz09hxgRkTcXuikkD70wMw64CwysypwFplZFVQxizzCzMxGTkTc23D7noZVNMDmmNmIchaZWRU4i8ysCqqYRVMGXaGZmZmZmZmZmVmVucPMzMzMzMzMzMwswx1mZma940MWzKwKnEVmVgXOIjOrgq6zyOcwMzPrnbnA9LIbYWYjz1lkZlXgLDKzKug6i9xhZmbWIxHxcNltMDNzFplZFTiLzKwKimSRD8k0MzMzMzMzMzPLcIeZmZmZmZmZmZlZhjvMzMzG8wlqzawKnEVmVgXOIjOrglKySBFRRr1mZpUkaRtgekQsKLstZja6nEVmVgXOIjOrgrKyyB1mZmZmZmZmZmZmGT4k08zMzMzMzMzMLMMdZmZmZmZmZmZmZhnuMDMzMzMzMzMzM8twh5mZmZmZmZmZmVmGO8zMzMzMzMzMzMwy/j8ZAjcMNe4fuwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b067a601b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoder Self Layer 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b067a8de6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoder Src Layer 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b067a6f3a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoder Self Layer 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b066d711b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoder Src Layer 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b067a62e908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tgt_sent = trans.split()\n",
    "def draw(data, x, y, ax):\n",
    "    seaborn.heatmap(data, \n",
    "                    xticklabels=x, square=True, yticklabels=y, vmin=0.0, vmax=1.0, \n",
    "                    cbar=False, ax=ax)\n",
    "    \n",
    "for layer in range(1, 6, 2):\n",
    "    fig, axs = plt.subplots(1,4, figsize=(20, 10))\n",
    "    print(\"Encoder Layer\", layer+1)\n",
    "    for h in range(4):\n",
    "        draw(model.encoder.layers[layer].self_attn.attn[0, h].data, \n",
    "            sent, sent if h ==0 else [], ax=axs[h])\n",
    "    plt.show()\n",
    "    \n",
    "for layer in range(1, 6, 2):\n",
    "    fig, axs = plt.subplots(1,4, figsize=(20, 10))\n",
    "    print(\"Decoder Self Layer\", layer+1)\n",
    "    for h in range(4):\n",
    "        draw(model.decoder.layers[layer].self_attn.attn[0, h].data[:len(tgt_sent), :len(tgt_sent)], \n",
    "            tgt_sent, tgt_sent if h ==0 else [], ax=axs[h])\n",
    "    plt.show()\n",
    "    print(\"Decoder Src Layer\", layer+1)\n",
    "    fig, axs = plt.subplots(1,4, figsize=(20, 10))\n",
    "    for h in range(4):\n",
    "        draw(model.decoder.layers[layer].self_attn.attn[0, h].data[:len(tgt_sent), :len(sent)], \n",
    "            sent, tgt_sent if h ==0 else [], ax=axs[h])\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "> Hopefully this code is useful for future research. Please reach out if you have any issues. If you find this code helpful, also check out our other OpenNMT tools.\n",
    "\n",
    "```\n",
    "@inproceedings{opennmt,\n",
    "  author    = {Guillaume Klein and\n",
    "               Yoon Kim and\n",
    "               Yuntian Deng and\n",
    "               Jean Senellart and\n",
    "               Alexander M. Rush},\n",
    "  title     = {OpenNMT: Open-Source Toolkit for Neural Machine Translation},\n",
    "  booktitle = {Proc. ACL},\n",
    "  year      = {2017},\n",
    "  url       = {https://doi.org/10.18653/v1/P17-4012},\n",
    "  doi       = {10.18653/v1/P17-4012}\n",
    "}\n",
    "```\n",
    "\n",
    "> Cheers,\n",
    "> srush"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{::options parse_block_html=\"true\" /}\n",
    "<div id=\"disqus_thread\"></div>\n",
    "<script>\n",
    "\n",
    "/**\n",
    "*  RECOMMENDED CONFIGURATION VARIABLES: EDIT AND UNCOMMENT THE SECTION BELOW TO INSERT DYNAMIC VALUES FROM YOUR PLATFORM OR CMS.\n",
    "*  LEARN WHY DEFINING THESE VARIABLES IS IMPORTANT: https://disqus.com/admin/universalcode/#configuration-variables*/\n",
    "/*\n",
    "var disqus_config = function () {\n",
    "this.page.url = PAGE_URL;  // Replace PAGE_URL with your page's canonical URL variable\n",
    "this.page.identifier = PAGE_IDENTIFIER; // Replace PAGE_IDENTIFIER with your page's unique identifier variable\n",
    "};\n",
    "*/\n",
    "(function() { // DON'T EDIT BELOW THIS LINE\n",
    "var d = document, s = d.createElement('script');\n",
    "s.src = 'https://harvard-nlp.disqus.com/embed.js';\n",
    "s.setAttribute('data-timestamp', +new Date());\n",
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    "<noscript>Please enable JavaScript to view the <a href=\"https://disqus.com/?ref_noscript\">comments powered by Disqus.</a></noscript>\n",
    "                            "
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    "<script>\n",
    "    /**\n",
    "     *  RECOMMENDED CONFIGURATION VARIABLES: EDIT AND UNCOMMENT THE SECTION BELOW TO INSERT DYNAMIC VALUES FROM YOUR PLATFORM OR CMS.\n",
    "     *  LEARN WHY DEFINING THESE VARIABLES IS IMPORTANT: https://disqus.com/admin/universalcode/#configuration-variables\n",
    "     */\n",
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    "    };\n",
    "    */\n",
    "    (function() {  // REQUIRED CONFIGURATION VARIABLE: EDIT THE SHORTNAME BELOW\n",
    "        var d = document, s = d.createElement('script');\n",
    "        \n",
    "        s.src = 'https://EXAMPLE.disqus.com/embed.js';  // IMPORTANT: Replace EXAMPLE with your forum shortname!\n",
    "        \n",
    "        s.setAttribute('data-timestamp', +new Date());\n",
    "        (d.head || d.body).appendChild(s);\n",
    "    })();\n",
    "</script>\n",
    "<noscript>Please enable JavaScript to view the <a href=\"https://disqus.com/?ref_noscript\" rel=\"nofollow\">comments powered by Disqus.</a></noscript>"
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